Posted: November 25th, 2022

Wireless Networks and Communication (Information Systems)

A small research report on a selected topic.

the instructions and what to do doc is attached and also an article on the same topic is attached.

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This research study requires in-depth reading and analysis of a single specific topic.

The topic is Mobile Edge Computing: (A Survey on Architecture and Computation Offloading)

Make a small report on that research paper which should contain the following:

• Introduction to the topic

• Introduction to that paper, their main work.

• Detail description of the methodologies employed in that paper.

• Comparison of the results reported in that paper with others.

• Your comments on the advantages/disadvantages/superiority of that paper’s methodology.

• Suggest your changes/future-directions for that paper to improve it (if any).

• Conclusion.

• References.

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7

Mobile Edge Computing: A Survey on Architecture

and Computation

Offloading

Pavel Mach, IEEE Member, Zdenek Becvar, IEEE Member

Abstract—Technological evolution of mobile user equipments
(UEs), such as smartphones or laptops, goes hand-in-hand with
evolution of new mobile applications. However, running compu

tationally demanding applications at the UEs is constrained by
limited battery capacity and energy consumption of the UEs.
Suitable solution extending the battery life-time of the UEs
is to offload the applications demanding huge processing to a
conventional centralized cloud (CC). Nevertheless, this option
introduces significant execution delay consisting in delivery of
the offloaded applications to the cloud and back plus time of
the computation at the cloud. Such delay is inconvenient and
make the offloading unsuitable for real-time applications. To
cope with the delay problem, a new emerging concept, known as
mobile edge computing (MEC), has been introduced. The MEC
brings computation and storage resources to the edge of mobile
network enabling to run the highly demanding applications
at the UE while meeting strict delay requirements. The MEC
computing resources can be exploited also by operators and third
parties for specific purposes. In this paper, we first describe
major use cases and reference scenarios where the MEC is
applicable. After that we survey existing concepts integrating
MEC functionalities to the mobile networks and discuss current
advancement in standardization of the MEC. The core of this
survey is, then, focused on user-oriented use case in the MEC,
i.e., computation offloading. In this regard, we divide the research
on computation offloading to three key areas: i) decision on
computation offloading, ii) allocation of computing resource
within the MEC, and iii) mobility management. Finally, we
highlight lessons learned in area of the MEC and we discuss
open research challenges yet to be addressed in order to fully
enjoy potentials offered by the

MEC.

I. INTRODUCTION

The users’ requirements on data rates and quality of service

(QoS) are exponentially increasing. Moreover, technologi-

cal evolution of smartphones, laptops and tablets enables

to emerge new high demanding services

and

applications.

Although new mobile devices are more and more powerful

in terms of central processing unit (CPU), even these may not

be able to handle the applications requiring huge processing

in a short time. Moreover, high battery consumption still

poses a significant obstacle restricting the users to fully enjoy

highly demanding applications on their own devices. This

motivates development of mobile cloud computing (MCC)

concept allowing cloud computing for mobile users [1]. In

the MCC, a user equipment (UE) may exploit computing

and storage resources of powerful distant centralized clouds

(CC), which are accessible through a core network (CN) of

This work has been supported by the grant of Czech Technical University
in Prague No. SGS17/184/OHK3/3T/13.

The authors are with the Department of Telecommunication Engi-
neering, Faculty of Electrical Engineering, Czech Technical University
in Prague, Prague, 166 27 Czech Republic (email:machp2@fel.cvut.cz;
zdenek.becvar@fel.cvut.cz).

a mobile operator and the Internet. The MCC brings several

advantages [2]; 1) extending battery lifetime by

offloading

energy consuming computations of the applications to the

cloud, 2) enabling sophisticated applications to the mobile

users, and 3) providing higher data storage capabilities to the

users. Nevertheless, the MCC also imposes huge additional

load both on radio and backhaul of mobile networks and

introduces high latency since data is sent to powerful farm

of servers that are, in terms of network topology, far away

from the users.

To address the problem of a long latency, the cloud services

should be moved to a proximity of the UEs, i.e., to the

edge of mobile network as considered in newly emerged

edge computing paradigm. The edge computing can be un-

derstood as a specific case of the MCC. Nevertheless, in

the conventional MCC, the cloud services are accessed via

the Internet connection [3] while in the case of the edge

computing, the computing/storage resources are supposed to

be in proximity of the UEs (in sense of network topology).

Hence, the MEC can offer significantly lower latencies and

jitter when compared to the MCC. Moreover, while the MCC

is fully centralized approach with farms of computers usually

placed at one or few locations, the edge computing is supposed

to be deployed in fully distributed manner. On the other hand,

the edge computing provides only limited computational and

storage resources with respect to the MCC. A high level

comparison of key technical aspects of the MCC and the edge

computing is outlined in Table I.

The first edge computing concept bringing the computa-

tion/storage closer to the UEs, proposed in 2009, is cloudlet

[4]. The idea behind the cloudlet is to place computers with

high computation power at strategic locations in order to

provide both computation resources and storage for the UEs

in vicinity. The cloudlet concept of the computing ”hotspots”

is similar to WiFi hotspots scenario, but instead of Internet

connectivity the cloudlet enables cloud services to the mobile

users. The fact that cloudlets are supposed to be mostly

accessed by the mobile UEs through WiFi connection is seen

TABLE I: High level comparison of MCC and Edge comput-

ing concepts

Technical aspect MCC Edge computing

Deployment Centralized Distributed

Distance to the UE High Low

Latency High Low

Jitter High Low

Computational power Ample Limited

Storage capacity Ample Limited

http://arxiv.org/abs/1702.05309v2

as a disadvantage since the UEs have to switch between

the mobile network and WiFi whenever the cloudlet services

are exploited [2]. Moreover, QoS (Quality of Service) of the

mobile UEs is hard to fulfill similarly as in case of the MCC,

since the cloudlets are not an inherent part of the mobile

network and coverage of WiFi is only local with limited

support of mobility.

The other option enabling cloud computing at the edge

is to perform computing directly at the UEs through ad-

hoc cloud allowing several UEs in proximity to combine

their computation power and, thus, process high demanding

applications locally [5]-[14]. To facilitate the ad-hoc cloud,

several critical challenges need to be addressed; 1) finding

proper computing UEs in proximity while guaranteeing that

processed data will be delivered back to the source UE, 2)

coordination among the computing UEs has to be enabled

despite the fact that there are no control channels to facil-

itate reliable computing, 3) the computing UEs has to be

motivated to provide their computing power to other devices

given the battery consumption and additional data transmission

constraints, 4) security and privacy issues.

A more general concept of the edge computing, when

compared to cloudlets and ad-hoc clouds, is known as a fog

computing. The fog computing paradigm (shortly often ab-

breviated as Fog in literature) has been introduced in 2012 by

Cisco to enable a processing of the applications on billions of

connected devices at the edge of network [15]. Consequently,

the fog computing may be considered as one of key enablers

of Internet of Things (IoT) and big data applications [16] as it

offers: 1) low latency and location awareness due to proximity

of the computing devices to the edge of the network, 2) wide-

spread geographical distribution when compared to the CC; 3)

interconnection of very large number of nodes (e.g., wireless

sensors), and 4) support of streaming and real time applica-

tions [15]. Moreover, the characteristics of the fog computing

can be exploited in many other applications and scenarios such

as smart grids, connected vehicles for Intelligent Transport

Systems (ITS) or wireless sensor networks [17]-[20].

From the mobile users’ point of view, the most notable

drawback of all above-mentioned edge computing concepts

is that QoS and QoE (Quality of Experience) for users can

be hardly guaranteed, since the computing is not integrated

into an architecture of the mobile network. One con

cept

integrating the cloud capabilities into the mobile network is

Cloud Radio Access Network (C-RAN) [21]. The C-RAN

exploits the idea of distributed protocol stack [22], where some

layers of the protocol stack are moved from distributed Radio

Remote Heads (RRHs) to centralized baseband units (BBUs).

The BBU’s computation power is, then, pooled together into

virtualized resources that are able to serve tens, hundreds or

even thousands of RRHs. Although the computation power

of this virtualized BBU pool is exploited primarily for a

centralized control and baseband processing it may also be

used for the computation offloading to the edge of the network

(see, for example, [23]).

Another concept integrating the edge computing into the

mobile network architecture is developed by newly created

(2014) industry specification group (ISG) within European

Telecommunications Standards Institute (ETSI) [24]. The so-

lution outlined by ETSI is known as Mobile Edge Com-

puting (MEC). The standardization efforts relating the MEC

are driven by prominent mobile operators (e.g., DOCOMO,

Vodafone, TELECOM Italia) and manufactures (e.g., IBM,

Nokia, Huawei, Intel). The main purpose of ISG MEC group

is to enable an efficient and seamless integration of the cloud

computing functionalities into the mobile network, and to help

developing favorable conditions for all stakeholders (mobile

operators, service providers, vendors, and users).

Several surveys on cloud computing have been published

so far. In [3], the authors survey MCC application models

and highlight their advantages and shortcomings. In [25],

a problem of a heterogeneity in the MCC is tackled. The

heterogeneity is understood as a variability of mobile de-

vices, different cloud vendors providing different services,

infrastructures, platforms, and various communication medium

and technologies. The paper identifies how this heterogeneity

impacts the MCC and discusses related challenges. The au-

thors in [26] survey existing efforts on Cloud Mobile Media,

which provides rich multimedia services over the Internet and

mobile wireless networks. All above-mentioned papers focus,

in general, on the MCC where the cloud is not allocated

specifically at the edge of mobile network, but it is accessed

through the Internet. Due to a wide potential of the MEC,

there is a lot of effort both in industry and academia focusing

on the MEC in particular. Despite this fact, there is just one

survey focusing primarily on the MEC [27] that, however,

only briefly surveys several research works dealing with the

MEC and presents taxonomy of the MEC by describing

key attributes. Furthermore, the authors in [28] extensively

surveys security issues for various edge computing concepts.

On top of that, the authors in [29] dedicate one chapter to the

edge computing, where applications of economic and pricing

models are considered for resource management in the edge

computing.

In contrast to the above-mentioned surveys, we describe

key use cases and scenarios for the MEC (Section II). Then,

we survey existing MEC concepts proposed in the literature

integrating the MEC functionalities into the mobile networks

and we discuss standardization of the MEC (Section III).

After that, the core part of the paper is focused on technical

works dealing with computation offloading to the MEC. On

one hand, the computation offloading can be seen as a key

use case from the user perspective as it enables running

new sophisticated applications at the UE while reducing its

energy consumption (see, e.g., [30]-[36] where computation

offloading to distant CC is assumed). On the other hand,

the computation offloading brings several challenges, such

as selection of proper application and programming models,

accurate estimation of energy consumption, efficient manage-

ment of simultaneous offloading by multiple users, or virtual

machine (VM) migration [37]. In this respect, we overview

several general principles related to the computation offload-

ing, such as offloading classification (full, partial offloading),

factors influencing the offloading itself, and management of

the offloading in practice (Section IV). Afterwards, we sort the

efforts within research community addressing following key

Fig. 1: Example of use cases and scenarios for the MEC.

challenges regarding computation offloading into the MEC:

• A decision on the computation offloading to the MEC

with the purpose to determine whether the offloading is

profitable for the UE in terms of energy consumption

and/or execution delay (Section V).

• An efficient allocation of the computing

resources

within the MEC if the computation is offloaded in order to

minimize execution delay and balance load of both com-

puting resources and communication links (Section VI).

• Mobility management for the applications offloaded

to the MEC guaranteeing service continuity if the UEs

exploiting the MEC roams throughout the network (Sec-

tion VII).

Moreover, we summarize the lessons learned from state of the

art focused on computation offloading to the MEC (Section

VIII) and outline several open challenges, which need to be

addressed to make the MEC beneficial for all stakeholders

(Section IX). Finally, we summarize general outcomes and

draw conclusions (Section X).

II. USE CASES AND SERVICE SCENARIOS

The MEC brings many advantages to all stakeholders, such

as mobile operators, service providers or users. As suggested

in [38][39], three main use case categories, depending on the

subject to which they are profitable to, can be distinguished for

the MEC (see Fig. 1). The next subsections discuss individual

use case categories and pinpoint several key service scenarios

and applications.

A. Consumer-oriented services

The first use case category is consumer-oriented and, hence,

should be beneficial directly to the end-users. In general, the

users profit from the MEC mainly by means of the computa-

tion offloading, which enables running new emerging applica-

tions at the UEs. One of the applications benefiting from the

computation offloading is a web accelerated browser, where

most of the browsing functions (web contents evaluation,

optimized transmission, etc.) are offloaded to the MEC; see

experimental results on offloading of web accelerated browser

to the MEC in [40]. Moreover, face/speech recognition or

image/video editing are also suitable for the MEC as these

require large amount of computation and storage [41].

Besides, the computation offloading to the MEC can be

exploited by the applications based on augmented, assisted or

virtual reality. These applications derive additional information

about users’ neighborhood by performing an analysis of their

surroundings (e.g., visiting tourists may find points of interest

in his/her proximity). This may require fast responses, and/or

significant amount of computing resources not available at

the UE. An applicability of the MEC for augmented reality

is shown in [42]. The authors demonstrate on a real MEC

testbed that the reduction of latency up to 88% and energy

consumption of the UE up to 93% can be accomplished by

the computation offloading to the MEC.

On top of that, the users running low latency applications,

such as online gaming or remote desktop, may profit from

the MEC in proximity. In this case a new instance of a

specific application is initiated at an appropriate mobile edge

host to reduce the latency and resources requirements of the

application at the UE.

B. Operator and third party services

The second use case category is represented by the services

from which operators and third parties can benefit. An example

of the use case profitable for the operator or third party is a

gathering of a huge amount of data from the users or sensors.

Such data is first pre-processed and analyzed at the MEC.

The pre-processed data is, then, sent to distant central servers

for further analysis. This could be exploited for safety and

security purposes, such as monitoring of an area (e.g., car

park monitoring).

Another use case is to exploit the MEC for IoT (Inter-

net of Thing) purposes [43]-[45]. Basically, IoT devices are

connected through various radio technologies (e.g., 3G, LTE,

WiFi, etc.) using diverse communication protocols. Hence,

there is a need for low latency aggregation point to handle

various protocols, distribution of messages and for processing.

This can be enabled by the MEC acting as an IoT gateway,

which purpose is to aggregate and deliver IoT services into

highly distributed mobile base stations in order to enable

applications responding in real time.

The MEC can be also exploited for ITS to extend the

connected car cloud into the mobile network. Hence, road-

side applications running directly at the MEC can receive

local messages directly from applications in the vehicles and

roadside sensors, analyze them and broadcast warnings (e.g.,

an accident) to nearby vehicles with very low latency. The

exploitation of the MEC for car-to-car and car-to-infrastructure

communications was demonstrated by Nokia and its partners

in an operator’s LTE network just recently in 2016 [46][47].

C. Network performance and QoE improvement services

The third category of use cases are those optimizing network

performance and/or improving QoE. One such use case is to

enable coordination between radio and backhaul networks. So

far, if the capacity of either backhaul or radio link is degraded,

the overall network performance is negatively influenced as

well, since the other part of the network (either radio or

backhaul, respectively) is not aware of the degradation. In

this respect, an analytic application exploiting the MEC can

provide real-time information on traffic requirements of the

radio/backhaul network. Then, an optimization application,

running on the MEC, reshapes the traffic per application or

re-routes traffic as required.

Another way to improve performance of the network is to

alleviate congested backhaul links by local content caching at

the mobile edge. This way, the MEC application can store

the most popular content used in its geographical area. If

the content is requested by the users, it does not have to be

transfered over the backhaul network.

Besides alleviation and optimization of the backhaul net-

work, the MEC can also help in radio network optimization.

For example, gathering related information from the UEs and

processing these at the edge will result in more efficient

scheduling. In addition, the MEC can also be used for mo-

bile video delivery optimization using throughput guidance

for TCP (Transmission Control Protocols). The TCP has an

inherent difficulty to adapt to rapidly varying condition on

radio channel resulting in an inefficient use of the resources.

To deal with this problem, the analytic MEC application can

provide a real-time indication on an estimated throughput to

a backend video server in order to match the application-level

coding to the estimated throughput.

III. MEC ARCHITECTURE AND STANDARDIZATION

This section introduces and compares several concepts for

the computation at the edge integrated to the mobile network.

First, we overview various MEC solutions proposed in the

literature that enable to bring computation close to the UEs.

Secondly, we describe the effort done within ETSI standard-

ization organization regarding the MEC. Finally, we compare

individual existing MEC concepts (proposed in both literature

and ETSI) from several perspectives, such as MEC control or

location of the computation/storage resources.

A. Overview of the MEC concept

In recent years, several MEC concepts with purpose to

smoothly integrate cloud capabilities into the mobile network

architecture have been proposed in the literature. This section

briefly introduces fundamental principles of small cell cloud

(SCC), mobile micro cloud (MMC), fast moving personal

cloud, follow me cloud (FMC), and CONCERT. Moreover,

the section shows enhancements/modifications to the network

architecture necessary for implementation of each

MEC con-

cept.

1) Small cell cloud (SCC): The basic idea of the SCC,

firstly introduced in 2012 by the European project TROPIC

[48][53], is to enhance small cells (SCeNBs), like microcells,

picocells or femtocells, by an additional computation and

storage capabilities. The similar idea is later on addressed in

SESAME project as well, where the cloud-enabled SCeNBs

supports the edge computing [49][50]. The cloud-enhanced

SCeNBs can pool their computation power exploiting network

function virtualization (NFV) [51][52] paradigm. Because a

high number of the SCeNBs is supposed to be deployed in

future mobile networks, the SCC can provide enough compu-

tation power for the UEs, especially for services/applications

having stringent requirements on latency (the examples of such

applications are listed in Section II-A).

In order to fully and smoothly integrate the SCC concept

into the mobile network architecture, a new entity, denoted as

a small cell manager (SCM), is introduced to control the SCC

[53]. The SCM is in charge of the management of the comput-

ing and/or storage resources provided by the SCeNBs. Since

the SCeNBs can be switched on/off at any time (especially

if owned by the users as in case of the femtocells), the SCM

performs dynamic and elastic management of the computation

resources within the SCC. The SCM is aware of the overall

cluster context (both radio and cloud-wise) and decides where

to deploy a new computation or when to migrate an on-going

computation to optimize the service delivery for the end-user.

Fig. 2: SCC architecture (MME – Mobility Management Entity, HSS – Home Subscriber Server, S-GW – Serving Gateway,

P-GW – Packet Gateway).

The computing resources are virtualized by means of Virtual

Machine (VM) located at the SCeNBs. An important aspect

regarding the architecture of the SCC is deployment of the

SCM (see Fig. 2). The SCM may be deployed in a centralized

manner either as a standalone SCM located within the RAN,

close to a cluster of the SCeNBs, or as an extension to a

MME [53][54]. Moreover, the SCM can be deployed also in a

distributed hierarchical manner, where a local SCM (L-SCM)

or a virtual L-SCM (VL-SCM) manages the computing and

storage resources of the SCeNBs’ clusters in vicinity while a

remote SCM (R-SCM), located in the CN, has resources of all

SCeNBs connected to the CN at its disposal [55] (see Fig. 2b).

2) Mobile micro clouds (MMC): The concept of the MMC

has been firstly introduced in [56]. Like the SCC, also the

MMC allows users to have instantaneous access to the cloud

services with a low latency. While in the SCC the computa-

tion/storage resources are provided by interworking cluster(s)

of the SCeNBs, the UEs exploit the computation resources

of a single MMC, which is typically connected directly to a

wireless base station (i.e., the eNB in the mobile network) as

indicated in Fig. 3. The MMC concept does not introduce any

control entity into the network and the control is assumed to be

Fig. 3: MMC architecture.

fully distributed in a similar way as the VL-SCM solution for

the SCC. To this end, the MMCs are interconnected directly

or through backhaul in order to guarantee service continuity

if the UEs move within the network to enable smooth VM

migration among the MMCs (see more detail on VM migration

in Section VII-B).

3) Fast moving personal cloud (MobiScud): The MobiScud

architecture [57] integrates the cloud services into the mobile

networks by means of software defined network (SDN) [58]

and NFV technologies whilst maintaining backward compati-

bility with existing mobile network. When compared to the

SCC and the MMC concepts, the cloud resources in the

MobiScud are not located directly at the access nodes such as

SCeNB or eNB, but at operator’s clouds located within RAN

or close to RAN (see Fig. 4). Still, these clouds are assumed

to be highly distributed similarly as in case of the SCC and

the MMC enabling the cloud service to all UEs in vicinity.

Analogously to the SCC, the MobiScud introduces a new

control entity, a MobiScud control (MC), which interfaces

with the mobile network, SDN switches and the cloud of

the operator. Basically, the MC has two functionalities: 1)

monitoring control plane signaling message exchange between

Fig. 4: MobiScud architecture [57].

mobile network elements to be aware of the UEs activity (e.g.,

handover) and 2) orchestrating and routing data traffic within

SDN enabled transport network to facilitate the ap

plication

offloading and the VM migration if the UE moves throughout

the network.

4) Follow me cloud (FMC): The key idea of the FMC

is that the cloud services running at distributed data centers

(DCs) follow the UEs as they roam throughout the network

[59][60] in the same way as in the case of the MobiScud.

When compared to the previous MEC concepts, the com-

puting/storage power is moved farther from the UEs; into

the CN network of the operator. Nevertheless, while previous

MEC concepts assume rather centralized CN deployment, the

FMC leverages from the fact that the mobile operators need

to decentralize their networks to cope with growing number

of the UEs. In this respect, the centralized CN used in the

current network deployment is assumed to be replaced by a

distributed one as shown in Fig. 5. For a convenience of the

mobile operators, the DC may be located at the same place as

the distributed S/P-GWs.

Similarly as the SCC and the MobiScud, the FMC intro-

duces new entities into the network architecture; a DC/GW

mapping entity and an FMC controller (FMCC). These can

be either functional entities collocated with existing network

nodes or a software run on any DC (i.e., exploiting NFV

principles like the SCC or MobiScud concepts). The DC/GW

mapping entity maps the DCs to the distributed S/P-GWs

according to various metrics, such as, location or hop count

between DC and distributed CN, in static or dynamic manner.

The FMCC manages DCs’ computation/storage resources,

cloud services running on them, and decides which DC should

be associated to the UE using the cloud services. The FMCC

may be deployed either centrally (as shown in Fig. 5) or

hierarchically [61] with global FMCC (G-FMCC) and local

FMCC (L-FMCC) for better scalability (controlled similarly

as in the SCC as explained in Section III-A1). Note that the

FMC itself may be also decentrally controlled by omitting

the FMCC altogether. In such a case, the DCs coordinate

themselves in a self-organizing manner.

Fig. 5: The network architecture enabling FMC concept (cen-

tralized solution).

Fig. 6: CONCERT architecture.

5) CONCERT: A concept converging cloud and cellular

systems, abbreviated as CONCERT, has been proposed in

[62]. The CONCERT assumes to exploit NFV principles and

SDN technology like above-mentioned solutions. Hence, the

computing/storage resources, utilized by both conventional

mobile communication and cloud computing services, are

presented as virtual resources. The control plain is basically

consisted of a conductor, which is a control entity man-

aging communication, computing, and storage resources of

the CONCERT architecture. The conductor may be deployed

centrally or in a hierarchical manner for better scalability

as in the SCC or FMC. The data plain consists of radio

interface equipments (RIEs) physically representing the eNB,

SDN switches, and computing resources (see Fig. 6). The

computing resources are used both for baseband processing

(similarly as in C-RAN) and for handling an application level

processing (e.g., for the application offloading). In all already

described MEC concepts, the computation/storage resources

have been fully distributed. The CONCERT proposes rather

hierarchically placement of the resources within the network to

flexibly and elastically manage the network and cloud services.

In this respect, local servers with a low computation power

are assumed to be located directly at the physical base station

(e.g., similarly as in the SCC or the MMC) and, if the local

resources are not sufficient, regional or even central servers

are exploited as indicated in Fig. 6.

B. ETSI MEC

Besides all above-mentioned solutions, also ETSI is cur-

rently deeply involved in standardization activities in order to

integrate the MEC into the mobile networks. In this regard,

we briefly summarize the standardization efforts on the MEC

within ETSI, describe reference architecture according to

ETSI, and contemplate various options for the MEC deploy-

ment that are considered so far.

1) Standardization of ETSI MEC: Standardization of the

MEC is still in its infancy, but drafts of specifications have

already been released by ISG MEC. The terminology used in

individual specifications relating to conceptual, architectural

and functional elements is described in [63]. The main purpose

of this document is to ensure the same terminology is used

by all ETSI specifications related to the MEC. A framework

exploited by ISG MEC for coordination and promotion of

MEC is defined in proof of concept (PoC) specification [64].

The basic objectives of this document is to describe the PoC

activity process in order to promote the MEC, illustrate key

aspects of the MEC and build a confidence in viability of

the MEC technology. Further, several service scenarios that

should benefit from the MEC and proximity of the cloud

services is presented in [65] (see Section II for more detail).

Moreover, technical requirements on the MEC to guarantee

interoperability and to promote MEC deployment are intro-

duced in [38]. The technical requirements are divided into

generic requirements, service requirements, requirements on

operation and management, and finally security, regulations

and charging requirements.

2) ETSI MEC reference architecture: The reference archi-

tecture, described by ETSI in [66], is composed of functional

elements and reference points allowing interaction among

them (see Fig. 7). Basically, the functional blocks may not

necessarily represent physical nodes in the mobile network, but

rather software entities running on the top of a virtualization

infrastructure. The virtualization infrastructure is understood

as a physical data center on which the VMs are run and the

VMs represent individual functional elements. In this respect,

it is assumed that some architectural features from ETSI NFV

group, which runs in parallel to ETSI MEC, will be reused for

the MEC reference architecture as well, since the basic idea

of NFV is to virtualize all network node functions.

As shown in Fig. 7, the MEC can be exploited either by

a UE application located directly in the UE, or by third

party customers (such as commercial enterprise) via customer

facing service (CFS) portal. Both the UE and the CFS portal

interact with the MEC system through a MEC system level

management. The MEC system level management includes a

user application lifecycle management (LCM) proxy, which

mediate the requests, such as initiation, termination or relo-

cations of the UE’s application within the MEC system to

Fig. 7: MEC reference architecture [66].

the operation support system (OSS) of the mobile operator.

Then, the OSS decides if requests are granted or not. The

granted requests are forwarded to a mobile edge orchestrator.

The mobile edge orchestrator is the core functionality in

the MEC system level management as it maintains overall

view on available computing/storage/network resources and

the MEC services. In this respect, the mobile edge orchestrator

allocates the virtualized MEC resources to the applications

that are about to be initiated depending on the applications

requirements (e.g., latency). Furthermore, the orchestrator also

flexibly scales down/up available resources to already running

applications.

The MEC system level management is interconnected with

a MEC server level management constituting a mobile edge

platform and a virtualization platform manager. The former

one manages the life cycle of the applications, application

rules and service authorization, traffic rules, etc. The latter

one is responsible for allocation, management and release of

the virtualized computation/storage resources provided by the

virtualization infrastructure located within the MEC server.

The MEC server is an integral part of the reference architecture

as it represents the virtualized resources and hosts the MEC

applications running as the VMs on top of the virtualization

infrastructure.

3) Deployment options of ETSI MEC: As already men-

tioned in the previous subsection, the MEC services will be

provided by the MEC servers, which have the computation and

storage resources at their disposal. There are several options

where the MEC servers can be deployed within the mobile

network. The first option is to deploy the MEC server directly

at the base station similarly as in case of the SCC or the MCC

(see Section III-A1 and Section III-A2). Note that in case of

a legacy network deployment, such as 3G networks, the MEC

servers may be deployed at 3G Radio Network Controllers

as well [38]. The second option is to place the MEC servers

at cell aggregation sites or at multi-RAT aggregation points

that can be located either within an enterprise scenario (e.g.,

company) or a public coverage scenario (e.g., shopping mall,

stadium, airport, etc.). The third option is to move the MEC

server farther from the UEs and locate it at the edge of CN

analogously to the FMC (Section III-A4).

Of course, selection of the MEC server deployment depends

on many factors, such as, scalability, physical deployment

constraints and/or performance criteria (e.g., delay). For ex-

ample, the first option with fully distributed MEC servers

deployment will result in very low latencies since the UEs

are in proximity of the eNB and, hence, in proximity of the

MEC server. Contrary, the UEs exploiting the MEC server

located in the CN will inevitably experience longer latencies

that could prevent a use of real-time applications. An initial

study determining where to optimally install the MEC servers

within the mobile network with the primary objective to find

a trade-off between installation costs and QoS measured in

terms of latency is presented in [67] and further elaborated

in [68]. Based on these studies, it is expected that, similarly

as in CONCERT framework (see Section III-A5), the MEC

servers with various computation power/storage capacities will

be scattered throughout the network. Hence, the UEs requiring

TABLE II: Comparison of existing MEC concepts.

MEC con-
cept

Control en-

tity

Control manner Control placement Computation/storage placement

SCC SCM Centralized, decentralized hierar-
chical (depending on SCM

type

and placement)

In RAN (e.g., at eNB) or in CN
(e.g., SCM collocated with MME)

SCeNB, cluster of SCeNBs

MMC – Decentralized MMC (eNB) eNB

MobiScud MC Decentralized Between RAN and CN Distributed cloud within RAN or
close to RAN

FMC FMCC Centralized, decentralized
hierarchical (option with
hierarchical FMCC), decentralized
(option without FMC controller)

Collocated with existing node (e.g.,
node in CN) or run as software on
DC

DC close or collocating with dis-
tributed CN

CONCERT Conductor Centralized, decentralized hierar-
chical

N/A (it could be done in the same
manner as in FMC concept)

eNB (RIE), regional and central
servers

ETSI MEC Mobile edge
orchestrator

Centralized N/A (the most feasible option is to
place control into CN

eNB, aggregation point, edge of
CN

only a low computation power will be served by the local

MEC servers collocated directly with the eNB, while highly

demanding applications will be relegated to more powerful

MEC servers farther from the UEs.

C. Summary

This section mutually compares the MEC concepts proposed

in literature with the vision of the MEC developed under ETSI.

There are two common trends followed by individual MEC

solutions that bring cloud to the edge of mobile network.

The first trend is based on virtualization techniques exploiting

NFVs principles. The network virtualization is a necessity in

order to flexibly manage virtualized resources provided by the

MEC. The second trend is a decoupling the control and data

planes by taking advantage of SDN paradigm, which allows a

dynamic adaptation of the network to changing traffic patterns

and users requirements. The use of SDN for the MEC is also in

line with current trends in mobile networks [69]-[71]. Regard-

ing control/signaling, the MMC and MobiScud assume fully

decentralize approach while the SCC, FMC, and CONCERT

adopt either fully centralized control or hierarchical control

for better scalability and flexibility.

If we compare individual MEC concepts in terms of com-

putation/storage resources deployment, the obvious effort is

to fully distribute these resources within the network. Still,

each MEC concept differs in the location, where the compu-

tation/storage resources are physically located. While the SCC,

MMC and MobiScud assume to place the computation close to

the UEs within RAN, the FMC solution considers integration

of the DCs farther away, for example, in a distributed CN.

On top of that, CONCERT distributes the computation/storage

resources throughout the network in a hierarchical manner so

that a low demanding computation application are handled

locally and high demanding applications are relegated either

to regional or central servers. Concerning ETSI MEC, there

are also many options where to place MEC servers offering

computation/storage resources to the UEs. The most probable

course of action is that the MEC servers will be deployed

everywhere in the network to guarantee high scalability of the

computation/storage resources. The comparison of all existing

MEC concepts is shown in Table II.

IV. INTRODUCTION TO COMPUTATION OFFLOADING

From the user perspective, a critical use case regarding the

MEC is a computation offloading as this can save energy

and/or speed up the process of computation. In general, a

crucial part regarding computation offloading is to decide

whether to offload or not. In the former case, also a question

is how much and what should be offloaded [41]. Basically, a

decision on computation offloading may result in:

• Local execution – The whole computation is done locally

at the UE (see Fig. 8). The offloading to the MEC is not

performed, for example, due to unavailability of the MEC

computation resources or if the offloading simply does not

pay off.

• Full offloading – The whole computation is offloaded and

processed by the MEC.

• Partial offloading – A part of the computation is processed

locally while the rest is offloaded to the MEC.

The computation offloading, and partial offloading in par-

ticular, is a very complex process affected by different factors,

such as users preferences, radio and backhaul connection

quality, UE capabilities, or cloud capabilities and availability

[3]. An important aspect in the computation offloading is also

an application model/type since it determines whether full or

partial offloading is applicable, what could be offloaded, and

how. In this regard, we can classify the applications according

to several criteria:

Fig. 8: Possible outcomes of computation offloading decision.

Fig. 9: An example of partial offloading for application

without non-offloadable part(s) (a) and application with non-

offloadable part (b).

• Offloadability of application – The application enabling

code or data partitioning and parallelization (i.e., applica-

tion that may be partially offloaded) can be categorized

into two types. The first type of the applications is the app,

which can be divided into N offloadable parts that all can
be offloaded (see Fig. 9a). Since each offloadable part may

differ in the amount of data and required computation, it

is necessary to decide which parts should be offloaded to

the MEC. In the example given in Fig. 9a, 1st, 2nd, 3nd,

6th, and 9th parts are processed locally while the rest is

offloaded to the MEC. Notice that in the extreme case, this

type of application may be fully offloaded to the MEC if

no parts are processed by the UE. The second type of the

applications is always composed of some non-offloadable

part(s) that cannot be offloaded (e.g., user input, camera, or

acquire position that needs to be executed at the UE [72])

and M offloadable parts. In Fig. 9b, the UE processes the
whole non-offloadable part together with 2nd, 6th, and 7th

parts while the rest of the application is offloaded to the

MEC.

Fig. 10: Dependency of offloadable components [72].

• Knowledge on the amount of data to be processed – The

applications can be classified according to the knowledge

on the amount of data to be processed. For the first type

of the applications (represented, e.g., by face detection,

virus scan, etc.,) the amount of data to be processed is

known beforehand. For the second type of the applications,

it is not possible to estimate the amount of data to be

processed as these are continuous-execution application and

there is no way to predict how long they will be running

(such as, online interactive games) [95]. It is obvious that

decision on computation offloading could be quite tricky

for continuous-execution application.

• Dependency of the offloadable parts – The last criterion

for classification of application to be offloaded is a mutual

dependency of individual parts to be processed. The parts

of the application can be either independent on each other

or mutually dependent. In the former case, all parts can

be offloaded simultaneously and processed in parallel. In

the latter case, however, the application is composed of

parts (components) that need input from some others and

parallel offloading may not be applicable. Note that the

relationship among individual components can be expressed

by component dependency graph (CDG) or call graph

(CG) (see, e.g., [34][41][72][73]). The relationship among

the components is illustrated in Fig. 10, where the whole

application is divided into M non-offloadable parts (1st, 4th,
and 6th part in Fig. 10) and N offloadable parts (2nd, 3rd,
and 5th part in Fig. 10). In the given example, 2nd and 3rd

part can be offloaded only after execution of the 1st part

while the 5th part can be offloaded after execution of the

1st – 4th parts.

The other important aspect regarding computation offload-

ing is how to utilize and manage offloading process in practice.

Basically, the UE needs to be composed of a code profiler,

system profiler, and decision engine [36]. The code profiler’s

responsibility is to determine what could be offloaded (depend-

ing on application type and code/data partitioned as explained

above). Then, the system profiler is in charge of monitoring

various parameters, such as available bandwidth, data size to

be offloaded or energy spent by execution of the applications

locally. Finally, decision engine determines whether to offload

or not.

The next sections survey current research works focus-

ing on following pivotal research topics: 1) decision on the

computation offloading to the MEC, 2) efficient allocation of

the computation resources within the MEC, and 3) mobility

management for the moving users exploiting MEC services.

Note that from now on we use explicitly the terminology

according to ETSI standardization activities. Consequently, we

use term MEC server as a node providing computing/storage

resources to the UEs instead of DC, MMC, etc.

V. DECISION ON COMPUTATION OFFLOADING TO MEC

This section surveys current research related to the

decision

on the computation offloading to the MEC. The papers are

divided into those considering either only the full offloading

(Section V-A) or those taking into account also possibility of

the partial offloading (Section V-B).

Fig. 11: The example of offloading decision aiming minimiza-

tion of

execution delay.

A. Full offloading

The main objective of the works focused on the full offload-

ing decision is to minimize an execution delay (Section V-A1),

to minimize energy consumption at the UE while predefined

delay constraint is satisfied (Section V-A2), or to find a

proper trade-off between both the energy consumption and

the execution delay (Section V-A3).

1) Minimization of execution delay: One of the advantages

introduced by the computation offloading to the MEC is a

possibility to reduce the execution delay (D). In case the
UE performs all computation by itself (i.e., no offloading

is performed), the execution delay (Dl) represents solely
the time spent by the local execution at the UE. In case

of the computation offloading to the MEC, the execution

delay (Do) incorporates three following parts: 1) transmission
duration of the offloaded data to the MEC (Dot), 2) compu-
tation/processing time at the MEC (Dop), and 3) time spent
by reception of the processed data from the MEC (Dor). The
simple example of the computation offloading decision based

solely on the execution delay is shown in Fig. 11. It could be

observed that the UE1 performs all computation locally since

the local execution delay is significantly lower than expected

execution delay for the computation offloading to the MEC

(i.e., Dl < Do). Contrary, a better alternative for the UE2 is to fully offload data to the MEC as the local execution would

result in notable higher execution delay (i.e., Dl > Do).
The goal to minimize execution delay is pursued by the au-

thors in [74]. This is accomplished by one-dimensional search

algorithm, which finds an optimal offloading decision policy

according to the application buffer queuing state, available

processing powers at the UE and at the MEC server, and

characteristic of the channel between the UE and the MEC

server. The computation offloading decision itself is done at

the UE by means of a computation offloading policy module

Fig. 12: Computation offloading considered in [74] (CSI stands

for channel state information).

(see Fig. 12). This module decides, during each time slot,

whether the application waiting in a buffer should be processed

locally or at the MEC while minimizing the execution delay.

The performance of the proposed algorithm is compared to

the local execution policy (computation done always locally),

cloud execution policy (computation performed always by the

MEC server), and greedy offloading policy (UE schedules

data waiting in the buffer whenever the local CPU or the

transmission unit is idle). The simulation results show that the

proposed optimal policy is able to reduce execution delay by

up to 80% (compared to local execution policy) and roughly

up to 44% (compared to cloud execution policy) as it is able to

cope with high density of applications’ arrival. The drawback

of the proposed method is that the UE requires feedback from

the MEC server in order to make the offloading decision, but

the generated signaling overhead is not discussed in the paper.

Another idea aiming at minimization of the execution de-

lay is introduced in [75]. When compared to the previous

study, the authors in [75] also reduce application failure

for the offloaded applications. The paper considers the UE

applies dynamic voltage and frequency scaling (DVS) [76]

and energy harvesting techniques [77] to minimize the energy

consumption during the local execution and a power control

optimizing data transmission for the computation offload-

ing. In this respect, the authors propose a low-complexity

Lyapunov optimization-based dynamic computation offloading

(LODCO) algorithm. The LODCO makes offloading decision

in each time slot and subsequently allocates CPU cycles for

the UE (if the local execution is performed) or allocates trans-

mission power (if the computation offloading is performed).

The proposed LODCO is able to reduce execution time by up

to 64% by offloading to the MEC. Furthermore, the proposal

is able to completely prevent a situation when offloaded

application would be dropped.

The drawback of both above-mentioned papers is that the

offloading decision does not take into account energy con-

sumption at the side of UE as fast battery depletion impose

significant obstacle in contemporary networks. In [75], the

energy aspect of the UE is omitted in the decision process

since the paper assumes that the UEs exploit energy harvesting

techniques. The harvesting technique, however, is not able to

fully address energy consumption problem by itself.

2) Minimization of energy consumption while satisfying

execution delay constraint: The main objective of the papers

surveyed in this section is to minimize the energy con-

sumption at the UE while the execution delay constraint of

the application is satisfied. On one hand, the computation

offloaded to the MEC saves battery power of the UE since

the computation does not have to be done locally. On the

other hand, the UE spends certain amount of energy in order

to: 1) transmit offloaded data for computation to the MEC

(Eot) and 2) receive results of the computation from the MEC
(Eor). The simple example of the computation offloading
decision primarily based on the energy consumption is shown

in Fig. 13. In the given example, the UE1 decides to perform

the computation locally since the energy spent by the local

execution (El) is significantly lower than the energy required
for transmission/reception of the offloaded data (E0). Contrary,

Fig. 13: The example of computation offloading decision based on energy consumption while satisfying execution delay

constraint.

the UE2 offloads data to the MEC as the energy required by the

computation offloading is significantly lower than the energy

spent by the local computation. Although the overall execution

delay would be lower if the UE1 offloads computation to

the MEC and also if the UE2 performs the local execution,

the delay is still below maximum allowed execution delay

constraint (i.e., Dl < Dmax). Note that if only the execution delay would be considered for the offloading decision (as

considered in Section V-A3), both UEs would unnecessarily

spent more energy.

The computation offloading decision minimizing the energy

consumption at the UE while satisfying the execution delay

of the application is proposed in [78]. The optimization prob-

lem is formulated as a constrained Markov decision process

(CMDP). To solve the optimization problem, two resource

allocation strategies are introduced. The first strategy is based

on an online learning, where the network adapts dynamically

with respect to the application running at the UE. The second

strategy is pre-calculated offline strategy, which takes advan-

tage of a certain level of knowledge regarding the application

(such as arrival rates measured in packets per slot, radio

channel condition, etc.). The numerical experiments show that

the pre-calculated offline strategy is able to outperform the

online strategy by up to 50% for low and medium arrival

rates (loads). Since the offline resource allocation strategy

proposed in [78] shows its merit, the authors devise two

addition dynamic offline strategies for the offloading [79]:

deterministic offline strategy and randomized offline

strategy.

It is demonstrated that both offloading offline strategies can

lead to significant energy savings comparing to the case when

the computing is done solely at the UE (energy savings up to

78%) or solely at the MEC (up to 15%).

A further extension of [79] from a single-UE to a multi-UEs

scenario is considered in [80]. The main objective is to jointly

optimize scheduling and computation offloading strategy for

each UE in order to guarantee QoE, fairness between the

UEs, low energy consumption, and average queuing/delay

constraints. The UEs that are not allowed to offload the

computation make either local computation or stay idle. It

is shown the offline strategy notably outperforms the online

strategies in terms of the energy saving (by roughly 50%).

In addition, the energy consumed by individual UEs strongly

depends on requirements of other UEs application.

Another offloading decision strategy for the multi-UEs

case minimizing the energy consumption at the UEs while

satisfying the maximum allowed execution delay is proposed

in [81]. A decision on the computation offloading is done

periodically in each time slot, during which all the UEs are

divided into two groups. While the UEs in the first group

are allowed to offload computation to the MEC, the UEs in

the second group have to perform computation locally due

to unavailable computation resources at the MEC (note that in

the paper, the computation is done at the serving SCeNB). The

UEs are sorted to the groups according to the length of queue,

that is, according to the amount of data they need to process.

After the UEs are admitted to offload the computation, joint

allocation of the communication and computation resources

is performed by finding optimal transmission power of the

UEs and allocation of the SCeNB’s computing resources to all

individual UEs. The performance of the proposal is evaluated

in terms of an average queue length depending on intensity of

data arrival and a number of antennas used at the UEs and the

SCeNB. It is shown that the more antennas is used, the less

transmission power at the UEs is needed while still ensuring

the delay constraint of the offloaded computation.

The main weak point of [81] is that it assumes only a single

SCeNB and, consequently, there is no interference among the

UEs connected to various SCeNBs. Hence, the work in [81]

is extended in [82] to the multi-cell scenario with N SCeNBs
to reflect the real network deployment. Since the formulated

optimization problem in [81] is no longer convex, the authors

propose a distributed iterative algorithm exploiting Successive

Convex Approximation (SCA) converging to a local optimal

solution. The numerical results demonstrate that the proposed

joint optimization of radio and computational resources signifi-

cantly outperforms methods optimizing radio and computation

separately. Moreover, it is shown that the applications with

fewer amount of data to be offloaded and, at the same time,

requiring high number of CPU cycles for processing are more

suitable for the computation offloading. The reason is that the

energy spent by the transmission/reception of the offloaded

data to the MEC is significantly lower than the energy savings

at the UE due to the computation offloading. The work in

[82] is further extended in [83] by a consideration of multi-

clouds that are associated to individual SCeNBs. The results

show that with an increasing number of the SCeNBs (i.e., with

increasing number of clouds), the energy consumption of the

UE proportionally decreases.

The same goal as in previous paper is achieved in [84] by

means of an energy-efficient computation offloading (EECO)

algorithm. The EECO is divided into three stages. In the

first stage, the UEs are classified according to their time and

energy cost features of the computation to: 1) the UEs that

should offload the computation to the MEC as the UEs cannot

satisfy the execution latency constraint, 2) the UEs that should

compute locally as they are able to process it by itself while

the energy consumption is below a predefined threshold, and

3) the UEs that may offload the computation or not. In the

second stage, the offloading priority is given to the UEs from

the first and the third set determined by their communication

channels and the computation requirements. In the third stage,

the eNBs/SCeNBs allocates radio resources to the UEs with

respect to given priorities. The computational complexity of

the EECO is O(max(I2 + N, IK + N)), where I is the
number of iterations, N stands for amount of UEs, and K
represents the number of available channels. According to

presented numerical results, the EECO is able to decrease

the energy consumption by up to 15% when compared to the

computation without offloading. Further, it is proofed that with

increasing computational capabilities of the MEC, the number

of UEs deciding to offload the computation increases as well.

3) Trade-off between energy consumption and execution

delay: The computation offloading decision for the multi-

user multi-channel environment considering a trade-off be-

tween the energy consumption at the UE and the execution

delay is proposed in [85]. Whether the offloading decision

prefers to minimize energy consumption or execution delay

is determined by a weighing parameter. The main objective

of the paper is twofold; 1) choose if the UEs should perform

the offloading to the MEC or not depending on the weighing

parameter and 2) in case of the computation offloading, select

the most appropriate wireless channel to be used for data

transmission. To this end, the authors present an optimal

centralized solution that is, however, NP-hard in the multi-

user multi-channel environment. Consequently, the authors

also propose a distributed computation offloading algorithm

achieving Nash equilibrium. Both the optimal centralized

solution and the distributed algorithm are compared in terms

of two performance metrics; 1) the amount of the UEs for

which the computation offloading to the MEC is beneficial and

2) the computation overhead expressed by a weighing of the

energy consumption and the execution delay. The distributed

algorithm performs only slightly worse than the centralized

one in both above-mentioned performance metrics. In addition,

the distributed algorithm significantly outperforms the cases

when all UEs compute all applications locally and when all

UEs prefer computing at the MEC (roughly by up to 40% for

50 UEs).

Other algorithm for the computation offloading decision

weighing the energy consumption at the UE and the execution

delay is proposed in [86]. The main difference with respect

to [85] is that the authors in [86] assume the computation

can be offloaded also to the remote centralized cloud (CC),

if computation resources of the MEC are not sufficient. The

computation offloading decision is done in a sequential man-

ner. In the first step, the UE decides whether to offload the

application(s) to the MEC or not. If the application is offloaded

to the MEC, the MEC evaluates, in the second step, if it is able

to satisfy the request or if the computation should be farther

relayed to the CC. The problem is formulated as a non-convex

quadratically constrained quadratic program (QCQP), which

is, however, NP-hard. Hence, a heuristic algorithm based on

a semi-definite relaxation together with a novel randomization

method is proposed. The proposed heuristic algorithm is able

to significantly lower a total system cost (i.e., weighted sum of

total energy consumption, execution delay and costs to offload

and process all applications) when compared to the situation

if the computation is done always solely at the UE (roughly

up to 70%) or always at the MEC/CC (approximately up to

58%).

The extension of [86] from the single-UE to the multi-

UEs scenario is presented in [87]. Since the multiple UEs

are assumed to be connected to the same computing node

(e.g., eNB), the offloading decision is done jointly with the

allocation of computing and communication resources to all

UEs. Analogously to [86], the proposal in [87] outperforms

the case when computation is done always by the UE (system

cost decreased by up to 45%) and strategy if computation is

always offloaded to the MEC/CC (system cost decreased by

up to 50%). Still, it would be useful to show the results for

more realistic scenario with multiple computing eNBs, where

interference among the UEs attached to different eNBs would

play an important role in the offloading decision. Moreover,

the overall complexity of the proposed solution is O(N6) per
one iteration, which could be too high for a high number of

UEs (N) connected to the eNB.

B. Partial offloading

This subsection focuses on the works dealing with the

partial offloading. We classify the research on works focused

on minimization of the energy consumption at the UE while

predefined delay constraint is satisfied (Section V-B1) and

works finding a proper trade-off between both the energy

consumption and the execution delay (Section V-B2).

1) Minimization of energy consumption while satisfying

execution delay constraint: This section focuses on the works

aiming on minimization of the energy consumption while

satisfying maximum allowable delay, similarly as in Sec-

tion V-A2. In [88], the authors consider the application divided

into a non-offloadable part and N offloadable parts as shown
in Fig. 9b. The main objective of the paper is to decide,

which offloadable parts should be offloaded to the MEC.

The authors propose an optimal adaptive algorithm based on

a combinatorial optimization method with complexity up to

O(2N). To decrease the complexity of the optimal algorithm,
also a sub-optimal algorithm is proposed reducing complexity

to O(N). The optimal algorithm is able to achieve up to
48% energy savings while the sub-optimal one performs only

slightly worse (up to 47% energy savings). Moreover, it is

shown that increasing SINR between the UE and the serving

eNBs leads to more prominent energy savings.

The minimization of the energy consumption while satis-

fying the delay constrains of the whole application is also

the main objective of [72]. Contrary to [88] the application

in [72] is supposed to be composed of several atomic parts

dependable on each other, i.e., some parts may be processed

only after execution of other parts as shown in Fig. 10 in

Section IV. The authors formulate the offloading problem as

0 − 1 programming model, where 0 stands for the application
offloading and 1 represents the local computation at the UE.
Nevertheless, the optimal solution is of a high complexity

as there exists 2N possible solutions to this problem (i.e.,
O(2N N2)). Hence, the heuristic algorithm exploiting Binary
Particle Swarm Optimizer (BPSO) [89] is proposed to reduce

the complexity to O(G.K.N2), where G is the number of
iterations, and K is the number of particles. The BPSO
algorithm is able to achieve practically the same results as

the high complex optimal solution in terms of the energy

consumption. Moreover, the partial offloading results in more

significant energy savings with respect to the full offloading

(up to 25% energy savings at the UE).

A drawback of both above papers focusing in detail on the

partial computation offloading is the assumption of only single

UE in the system. Hence, in [90], the authors address the

partial offloading decision problem for the multi-UEs scenario.

With respect to [72][88], the application to be offloaded does

not contain any non-offloadable parts and, in some extreme

cases, the whole application may be offloaded if profitable

(i.e., the application is structured as illustrated in 9a). The

UEs are assumed to be able to determine whether to partition

the application and how many parts should be offloaded to

the MEC. The problem is formulated as a nonlinear constraint

problem of a high complexity. As a consequence, it is sim-

plified to the problem solvable by linear programming and

resulting in the complexity O(N) (N is the number of UEs
performing the offloading). If the optimal solution applying

exhaustive search is used, 40% energy savings are achieved

when compared to the scenario with no offloading. In case of

the heuristic low complex algorithm, 30% savings are observed

for the UEs. The disadvantage of the proposal is that it assumes

the UEs in the system have the same channel quality and

all of them are of the same computing capabilities. These

assumptions, however, are not realistic for the real network.

A multi-UEs scenario is also assumed in [91], where the

authors assume TDMA based system where time is divided

into slots with duration of T seconds. During each slot, the
UEs may offload a part of their data to the MEC according to

their channel quality, local computing energy consumption,

and fairness among the UEs. In this regard, an optimal

resource allocation policy is defined giving higher priority to

those UEs that are not able to meet the application latency

constraints if the computation would be done locally. After

that, the optimal resource allocation policy with threshold

based structure is proposed. In other words, the optimal policy

makes a binary offloading decision for each UE. If the UE

has a priority higher than a given threshold, the UE performs

full computation offloading to the MEC. Contrary, if the

UE has a lower priority than the threshold, it offloads only

minimum amount of computation to satisfy the application

latency constraints. Since the optimal joint allocation of com-

munication and computation resources is of a high complexity,

the authors also propose a sub-optimal allocation algorithm,

which decouples communication and computation resource

allocation. The simulation results indicate this simplification

leads to negligibly higher total energy consumption of the UE

when compared to the optimal allocation. The paper is further

extended in [92], where the authors show that OFDMA access

enables roughly ten times higher energy savings achieved by

the UEs comparing to TDMA system due to higher granularity

of radio resources.

In all above-mentioned papers on partial offloading, the min-

imization of UE’s energy consumption depends on the quality

of radio communication channel and transmission power of

the UE. Contrary, in [93], the minimization of energy con-

sumption while satisfying execution delay of the application

is accomplished through DVS technique. In this respect, the

authors propose an energy-optimal partial offloading scheme

that forces the UE adapt its computing power depending on

maximal allowed latency of the application (LMAX ). In other
words, the objective of the proposed scheme is to guarantee

that the actual latency of the application is always equal

to LMAX . As a consequence, the energy consumption is
minimized while perceived QoS by the users is not negatively

affected.

2) Trade-off between energy consumption and execution

delay: A trade-off analysis between the energy consumption

and the execution delay for the partial offloading decision is

delivered in [94]. Similarly as in [90], the application to be

offloaded contains only offloadable parts and in extreme case,

the full offloading may occur (as explained in Section V-B).

The offloading decision considers the following parameters:

1) total number of bits to be processed, 2) computational

capabilities of the UE and the MEC, 3) channel state between

the UE and the serving SCeNB that provides access to the

MEC, and 4) energy consumption of the UE. The computation

offloading decision is formulated as a joint optimization of

communication and computation resources allocation. The

simulation results indicate that the energy consumption at

the UE decreases with increasing total execution time. This

decrease, however, is notable only for small execution time

duration. For a larger execution time, the gain in the energy

savings is inconsequential. Moreover, the authors show the

offloading is not profitable if the communication channel is

of a low quality since a high amount of energy is spent to

offload the application. In such situation, the whole application

is preferred to be processed locally at the UE. With an

intermediate channel quality, a part of the computation is

offloaded to the MEC as this results in energy savings. Finally,

if the channel is of a high quality, the full offloading is

preferred since the energy consumption for data transmission

is low while the savings accomplished by the computation

offloading are high.

The study in [95] provides more in-depth theoretical anal-

ysis on trade-off between the energy consumption and the

latency of the offloaded applications preliminarily handled

in [94]. Moreover, the authors further demonstrate that a

probability of the computation offloading is higher for good

channel quality. With higher number of antennas (4×2 MIMO

and 4×4 MIMO is assumed), the offloading is done more often

and the energy savings at the UE are more significant when

compared to SISO or MISO (up to 97% reduction of energy

TABLE III: The comparison of individual papers addressing computation offloading decisions.

Offloading
type

Objective Proposed solution No. of UE

offloading

Evaluation

method

Reduction of

D/EUE wrt local
computing

Complexity

of proposed
algorithm

[74] Full 1) Minimize D One-dimensional search algorithm
finding the optimal offloading pol-
icy

Single UE Simulations Up to 80% reduc-
tion of D

N/A

[75] Full 1) Minimize D, 2)
Minimize application
failure

Lyapunov optimization-based dy-
namic computation offloading

Single UE Theoretical
verifications,
simulations

Up to 64% reduction
of D

N/A

[78] Full 1) Minimize EUE, 2)
Satisfy D constraint

Online learning allocation strategy,
offline pre-calculated strategy

Single UE Simulations Up to 78% reduction
of EUE

N/A

[79] Full 1) Minimize EUE, 2)
Satisfy D constraint

Deterministic and random offline
strategies

Single UE Simulations Up to 78% reduction
of EUE
N/A

[80] Full 1) Minimize EUE, 2)
Satisfy D constraint

Deterministic offline strategy, de-
terministic online strategy based on
post-decision learning framework

Multi UEs Simulations N/A N/A

[81] Full 1) Minimize EUE, 2)
Satisfy D constraint

Joint allocation of communication
and computation resources

Multi UEs Simulations N/A N/A

[82] Full 1) Minimize EUE, 2)
Satisfy D constraint

Distributed iterative algorithm ex-
ploiting Successive Convex Ap-
proximation (SCA)

Multi UEs Simulations N/A N/A

[83] Full 1) Minimize EUE, 2)
Satisfy D constraint

Distributed iterative algorithm ex-
ploiting Successive Convex Ap-
proximation (SCA)
Multi UEs Simulations N/A N/A

[84] Full 1) Minimize EUE, 2)
Satisfy D constraint

Energy-efficient computation of-
floading (EECO) algorithm

Multi UEs Simulations Up to 15% reduction
of EUE

O(max(I2+
N, IK +N))

[85] Full 1) Trade-off between
EUE and D

Computation offloading game Multi UEs Analytical
evaluations,
simulations

Up to 40% reduction
of EUE

N/A

[86] Full 1) Trade-off between
EUE and D

Heuristic algorithm based on
semidefinite relaxation and
randomization mapping method

Single UE Simulations Up to 70% reduction
of total cost

N/A

[87] Full 1) Trade-off between
EUE and D

Heuristic algorithm based on
semidefinite relaxation and
randomization mapping method

Multi UEs Simulations Up to 45% reduction
of total cost

O(N6) per
iteration

[88] Partial 1) Minimize EUE, 2)
Satisfy D constraint

Adaptive algorithm based on com-
binatorial optimization method

Single UE Simulations Up to 47% reduction
of EUE

O(N)

[72] Partial 1) Minimize EUE, 2)
Satisfy D constraint

Algorithm exploiting binary parti-
cle swarm optimizer

Single UE Simulations Up to 25% reduction
of EUE

O(G.K.N2)

[90] Partial 1) Minimize EUE, 2)
Satisfy D constraint

Application and delay based re-
source allocation scheme

Multi UEs Simulations Up to 40% reduction
of EUE

O(N)

[91] Partial 1) Minimize EUE, 2)
Satisfy D constraint

Optimal resource allocation policy
with threshold based structure for
TDMA system

Multi UEs Simulations N/A N/A

[92] Partial 1) Minimize EUE, 2)
Satisfy D constraint

Optimal resource allocation policy
with threshold based structure for
TDMA and OFDMA system

Multi UEs Simulations N/A O(K + N)

[93] Partial 1) Minimize EUE, 2)
Satisfy D constraint

Adapting computing power of the
UE by means of DVS to achieve
maximum allowed latency

Single UE Simulations N/A N/A

[94] Partial 1) Trade-off between
EUE and D

Joint allocation of communication
and computational resources

Single UE Simulations N/A N/A

[95] Partial 1) Trade-off between
EUE and D

Iterative algorithm finding the op-
timal value of the number of bits
sent in uplink

Single UE Analytical
evaluations,
simulations

Up to 97% reduction
of EUE (SINR 45
dB, 4×4 MIMO)

N/A

[96] Partial 1) Trade-off between
EUE and D

Joint allocation of communication
and computational resources

Multi UEs Simulations Up to 90% reduction
of EUE

N/A

[97] Partial 1) Trade-off between
EUE and D

Lyapunov optimization-based dy-
namic computation offloading

Multi UEs Simulations Up to 90% reduction
of EUE, up to 98%
reduction of D

N/A

consumption for 4×4 MIMO antenna configuration). Note that

the same conclusion is also reached, e.g., in [81][82].

The main drawback in [94][95] is that these papers consider

only the single-UE scenario. A trade-of analysis between the

energy consumption at the UE and the execution delay for the

multi-UEs scenario is delivered in [96]. In case of the multi-

UEs scenario, the whole joint optimization process proposed

in [95] has to be further modified since both communication

and computation resources provided by the MEC are shared

among multiple UEs. In the paper, it is proven that with more

UEs in the system, it takes more time to offload the application

and it also lasts longer to process the application in the MEC.

The reason for this phenomenon is quite obvious since less

radio and computational resources remains for each UE. Still,

up to 90% of energy savings may be accomplished in multi-

UE scenario.

A trade-off between the power consumption and the execu-

tion delay for the multi-UEs scenario is also tackled in [97].

The authors formulate a power consumption minimization

problem with application buffer stability constraints. In this re-

gard, the online algorithm based on Lyapunov optimization is

proposed to decide on optimal CPUs frequency for those UEs

performing the local execution and to allocate transmission

power and bandwidth to the UEs offloading the application

to the MEC. The proposed algorithm is able to control the

power consumption and the execution delay depending on the

selected priority. The paper also demonstrates that the use of

the MEC for the computation offloading is able to bring up to

roughly 90% reduction in the power consumption while the

execution delay is reduced approximately by 98%.

C. Summary of works focusing on computation offloading

decision

A comparison of individual computation offloading strate-

gies is illustrated in Table III. The majority of computation

offloading decision algorithms aims to minimize the energy

consumption at the UE (EUE) while satisfying the execu-
tion delay (D) acceptable by the offloaded application or
to find/analyse a trade-off between these two metrics. The

papers indicate up to 90% energy savings achievable by the

computation offloading to the MEC and execution delay may

be reduced even up to 98%. Besides, all the papers evaluate

the proposed solutions mostly by means of simulation (only

several studies perform analytical evaluations).

VI. ALLOCATION OF COMPUTING RESOURCES

If a decision on the full or partial offloading of an applica-

tion to the MEC is taken (as discussed in previous section), a

proper allocation of the computation resources has to be done.

Similarly as in case of the computation offloading decision, the

selection of computation placement is influenced by the ability

of the offloaded application to be parallelized/partitioned.

If the parallelization/partitioning of the application is not

possible, only one physical node may be allocated for the

computing since the application cannot be split into several

parts (in Fig. 14, the UE1 offloads whole application to the

eNB as this application cannot be partitioned). In the opposite

case, the offloaded application may be processed by resources

distributed over several computing nodes (in Fig. 14, the

application offloaded by the UE2 is partitioned and processed

by all three eNBs).

This section surveys the papers addressing the problem

of a proper allocation of the computing resources for the

applications that are going to be offloaded to the MEC (or

in some cases to the CC, if the MEC computing resources

are not sufficient). We categorize the research in this area into

papers focusing on allocation of the computation resources at

1) a single computing node (Section VI-A) and 2) multiple

computing nodes (

Section VI-B).

A. Allocation of computation resources at a single node

The maximization of the amount of the applications served

by the MEC while satisfying the delay requirements of the

offloaded applications is the main objective in [98]. The

Fig. 14: An example of allocation of computing resources

within the MEC.

decision where the individual applications should be placed

depends on the applications priorities (derived from the ap-

plication’s delay requirements, i.e., the application with a low

delay requirements has higher priority) and availability of the

computing resources at the MEC. The basic principle for the

allocation of computation resources is depicted in Fig. 15.

The offloaded applications are firstly delivered to the local

scheduler within the MEC. The scheduler checks if there is

a computing node with sufficient computation resources. If

there is a computing node with enough available resources,

the VM is allocated at the node. Then the application is

processed at this MEC node, and finally sent back to the UE

(see Fig. 15). However, if the computation power provided

by the MEC server is not sufficient, the scheduler delegates

the application to the distant CC. In order to maximize the

amount of applications processed in the MEC while satisfying

their delay requirements, the authors propose a priority based

cooperation policy, which defines several buffer thresholds for

each priority level. Hence, if the buffer is full, the applications

are sent to the CC. The optimal size of the buffer thresholds is

found by means of low-complexity recursive algorithm. The

proposed cooperation policy is able to increase the probability

of the application completion within the tolerated delay by

25%.

When compared to the previous paper, the general objective

of [99] is to minimize not only the execution delay but also

the power consumption at the MEC. The paper considers a

hot spot area densely populated by the UEs, which are able

to access several MEC servers through nearby eNBs. To that

Fig. 15: Allocation of computation resources according to [98].

end, an optimal policy is proposed using equivalent discrete

MDP framework. However, this method results in a high

communication overhead and high computational complexity

with increasing number of the MEC servers. Hence, this

problem is overcome by developing an application assignment

index policy. In this respect, each eNB calculates its own index

policy according to the state of its computing resources. Then,

this index policy is broadcasted by all eNBs and the UE is able

to select the most suitable MEC server in order to minimize

both execution delay and power consumption. According to

the results, the index policy is in the worst case by 7% more

costly than optimal policy in terms of system cost (note that

system cost represents weighted execution delay and power

consumptions of the MEC).

The minimization of the execution delay of the offloaded

application is also the main goal in [100]. Nonetheless, with

respect to [98][99], the other objectives are to minimize

both communication and computing resource overloading and

the VM migration cost (note that in [100], the computing

nodes are represented by the SCeNBs and the VM migration

may be initiated due to the SCeNBs shutdown). The whole

problem is formulated as the VM allocation at the SCeNB and

solved by means of MDP. An example of the VM allocation

according to [100] is shown in Fig. 16, where the VM for the

UE1 is allocated at the serving SCeNB1 while the UE2 has

allocated the VM at the neighbouring SCeNB3. The SCeNB3

is preferred because of a high quality backhaul resulting in a

low transmission delay of the offloaded data. The simulations

show that with higher VM migration cost, the VM is preferred

to be allocated at the serving SCeNB (i.e., the SCeNB closest

to the UE) if this SCeNB has enough computation power.

The main disadvantage of all above-mentioned approaches

is that these do not consider more computing nodes within

the MEC for single application in order to further decrease its

execution delay.

B. Allocation of computation resources at multiple nodes

(federated clouds)

When compared to the previous section, the allocation of

computation resources at multiple computing nodes is con-

sidered here. The papers are split into subsections according

Fig. 16: An example of the VM allocation at single computing

SCeNB according to [100].

Fig. 17: An example of allocation of computation resources

for individual UEs according to [101].

to the main objective: 1) minimize execution delay and/or

power consumption of computing nodes (Section VI-B1) and

2) balance both communication and computing loads (Sec-

tion VI-B1).

1) Minimization of execution delay and/or power consump-

tion of computing nodes: The minimization of the execution

delay by allocation of computing resources provided by the

cluster of SCeNBs while avoiding to use the CC is proposed in

[101]. The cluster formation is done by means of a cooperative

game approach, where monetary incentives are given to the

SCeNBs if they perform the computation for the UEs attached

to other SCeNBs. The coalition among the SCeNBs is formed

for several time slots and then new coalitions may be created.

The allocation of computation resources is done as shown in

Fig. 17. Firstly, the serving SCeNB tries to serve their UEs on

its own since this results in the shortest communication delay

(e.g., in Fig. 17 SCeNB1 allocates the computation resources

to the UE1 and the UE2, etc.). Only if the SCeNB is not

able to process the application on its own, it is forwarded to

all SCeNBs in the same cluster (in Fig. 17, the computation

for the UE3 is done at the SCeNB2 and the SCeNB3). The

numerical results show that the proposed scheme is able to

reduce the execution delay by up to 50% when compared

to the computation only at the serving SCeNB and by up

to 25% comparing to the scenario when all SCeNBs in

the system participate in the computation. Unfortunately, the

proposed approach does not address a problem of forming new

coalitions and its impact on currently processed applications.

The selection of computing nodes can significantly influence

not only the execution delay, as considered in [101], but also

the power consumption of the computing nodes. Hence, the

main objective of [102] is to analyze an impact of the cluster

size (i.e., the amount of the SCeNBs performing computing)

on both execution latency of the offloaded application and

the power consumption of the SCeNBs. The analysis is done

for different backhaul topologies (ring, tree, full mesh) and

technologies (fiber, microwave, LTE). The authors demonstrate

that full mesh topology combined with fiber or microwave

connection is the most profitable in terms of execution latency

(up to 90% execution delay reduction). Contrary, a fiber back-

haul in ring topology results in the lowest power consumption.

Moreover, the paper shows that an increasing number of the

computing SCeNBs does not always shorten execution delay.

Quite the opposite, if a lot of SCeNBs process the offloading

applications and the transmission delay becomes longer than

the computing delay at the SCeNBs, the execution delay may

be increased instead. Besides, with an increasing number of

the computing SCeNBs, power consumption increases as well.

Consequently, a proper cluster formation and the SCeNBs

selection play a crucial part in system performance.

The problem to find an optimal formation of the clusters of

SCeNBs for computation taking into account both execution

delay and power consumption of the computing nodes is ad-

dressed in [103]. The paper proposes three different clustering

strategies. The first clustering strategy selects the SCeNBs in

order to minimize execution delay. Since all SCeNBs in the

system model are assumed to be one hop away (i.e., full mesh

topology is considered), basically all SCeNBs are included in

the computation resulting in up to 22% reduction of execution

delay. This is due to the fact that the computation gain

(and, thus, increase in the offloaded application processing)

is far greater than the transmission delay. The objective of

the second clustering strategy is to minimize overall power

consumption of the cluster. In this case, only the serving

SCeNB is preferred to compute, thus, any computation at

the neighbouring SCeNBs is suppressed to minimize power

consumption of the SCeNBs (up to 61% reduction of power

consumption is observed). This, however, increases overall

latency and high variations of the computation load. The last

clustering strategy aims to minimize the power consumption

of each SCeNB in the cluster, since the power consumptions

of the individual SCeNBs is highly imbalanced in the second

strategy.

While in [103] the optimal clustering of the SCeNBs is

done only for single UE, the multi UEs scenario is assumed

in [104]. When compared to the previous paper, whenever the

UE is about to offload data for the computation, the computing

cluster is assigned to it. Consequently, each UE has assigned

different cluster size depending on the application and the

UE’s requirements. The core idea of the proposal is to jointly

compute clusters for all active users’ requests simultaneously

to being able efficiently distribute computation and communi-

cation resources among the UEs and to achieve higher QoE.

The main objective is to minimize the power consumption of

the clusters while guaranteeing required execution delay for

each UE. The joint clusters optimization is able to significantly

outperform the successive cluster optimization (allocation of

the clusters are done subsequently for each UE), the static

clustering (equal load distribution among SCeNBs) and no

clustering (computation is done only by the serving SCeNB)

in terms of the users’ satisfaction ratio (up to 95% of UEs is

satisfied). On the other hand, the average power consumption

is significantly higher when compared to ”no clustering” and

”successive clusters optimization” scenarios.

Similar as in [104], the multi-UE cluster allocation is

assumed in [105], but the cluster formation is done jointly with

the UEs scheduling. The proposed resource allocation process

is split into two steps similarly as proposed in [101]. In the

first step, labeled as local computational resource allocation,

each SCeNB allocates its computational resources to their

own UEs according to specific scheduling rules, such as

application latency constraint, computation load or minimum

required computational capacity. In the second step, labelled as

establishment of computing clusters, the computation clusters

are created for each UE that cannot be served by its serving

SCeNB. The authors propose three algorithm realizations

differing in applications prioritization (e.g., earliest deadline

first or according to computation size of application) and the

objective (minimization of power consumption or execution

latency similarly as, e.g., in [104]). The simulations illustrate

that there could be found the algorithm realization resulting

in the users satisfaction ratio above 95% while keeping a

moderate power consumption of all computing nodes.

2) Balancing of communication and computation load: In

the previous section, the allocation of computing resources

is done solely with purpose to minimize the execution delay

and/or the power consumption of the computing nodes. This

could, however, result in unequal load distribution among

individual computing nodes and backhaul overloading. The

balancing of communication and computation load of the

SCeNBs while satisfying the delay requirement of the of-

floaded application is addressed in [106]. To this end, an

Application Considering Algorithm (ACA) selecting suitable

SCeNBs according to the current computation and commu-

nication load of the SCeNBs is proposed. The ACA exploits

knowledge of the offloaded application’s requirements (i.e.,

the number of bytes to be transferred and the maximum

latency acceptable by the application/user). The selection of

the SCeNBs for the computation is done in a static way

prior to the offloading to avoid expensive VMs migration.

The performance evaluation is done for two backhauls, low

throughput ADSL and high quality gigabit passive optical

network (GPON). The proposed ACA algorithm is able to

satisfy 100% of the UEs as long as number of offloaded tasks

per second is up to 6. Moreover, the paper shows that tasks

parallelization helps to better balance computation load.

Fig. 18: An example of application and physical graph accord-

ing to [107] (FD – Face detection, IPFE – Image processing

and feature extraction, FR – Face recognition, D – Database).

TABLE IV: The comparison of individual papers addressing allocation of computation resources for application/data already

decided to be offloaded.

No. of com-
puting nodes

for each ap-

plication

Objective Proposed solution Computing
nodes

Evaluation
method

Results

[98] Single node 1) Maximize the amount of
served applications, 2) Sat-
isfy D constraint

Priority based cooperation policy

MEC servers
(e.g., at the eNB
or agg. point), CC

Simulations 25% reduction of D
wrt offloading only
to the MEC

[99] Single node 1) Minimize D, 2) Mini-
mize EC

Optimal online application assign-
ment policy using equivalent dis-
crete MDP framework

MEC servers
(e.g., at the eNB
or agg. point), CC

Simulations N/A

[100] Single node 1) Minimize D, 2) Mini-
mize overloading of com-
munication and computing
resources, 3) Minimize VM
migration cost

Optimal allocation policy obtained
by solving MDP using linear pro-
graming reformulation

SCeNBs Simulations N/A

[101] Multiple nodes 1) Minimize D, 2) Avoid
to use the CC due to high
delay

Formation of collaborative coali-
tions by giving monetary incentives
to the SCeNBs

SCeNBs Simulations Up to 50% reduction
of D wrt single com-
puting SCeNB

[102] Multiple nodes 1) Analyze the impact of
different network topologies
and technologies on execu-
tion delay and power con-
sumption

N/A SCeNBs Simulations Up to 90% reduction
of D wrt single com-
puting SCeNB

[103] Multiple nodes 1) Minimize D, 2) Mini-
mize EC

Three clustering strategies mini-
mizing delay, power consumption
of the cluster and power consump-
tion of the SCs

SCeNBs Simulations 22% reduction of
D, 61% reduction of
EC

[104] Multiple nodes 1) Minimize D, 2) Mini-
mize EC

Joint cluster formation for all active
users requests simultaneously

SCeNBs Simulations Up to 95% of UE
are satisfied (for
max. 5 UEs)

[105] Multiple nodes 1) Minimize D, 2) Mini-
mize EC

Joint cluster formation for all ac-
tive users requests simultaneously
together with users scheduling

SCeNBs Simulations Up to 95% of UE
are satisfied (for
max. 5 UEs)

[106] Multiple nodes 1) Balance communication
and computation load of
computing nodes, 2) Satisfy
execution delay requirement

ACA algorithm assuming jointly
computation and communication
loads

SCeNBs Simulations 100% satisfaction
ratio for up to 6
offloaded tasks/s

[107] Multiple nodes 1) Balance communication
and computation load of
computing nodes, 2) Mini-
mize resource utilization

Online approximation algorithms
with polynomial-logarithmic (poly-
log) competitive ratio for tree ap-
plication graph placement

UE, eNB, CC Simulations Reduction of
resource utilization
up to 10%

The main objective to balance the load (both communi-

cation and computation) among physical computing nodes

and, at the same time, to minimize the resource utilization

of each physical computing node (i.e., reducing sum resource

utilization) is also considered in [107]. The overall problem is

formulated as a placement of application graph onto a physical

graph. The former represents the application where nodes in

graph correspond to individual components of the application

and edges to the communication requirements between them.

The latter represents physical computing system, where the

nodes in graph are individual computing devices and edges

stands for the capacity of the communication links between

them (see the example of application and physical graphs

in Fig. 18 for the face recognition application). The authors

firstly propose the algorithm finding the optimal solution for

the linear application graph and, then, more general online

approximation algorithms. The numerical results demonstrate

that the proposed algorithm is able to outperform two heuristic

approaches in terms of resource utilization by roughly 10%.

C. Summary of works dealing with allocation of computing

resources

The comparison of individual methods addressing allocation

of the computation resources within the MEC is shown in

Table IV. The main objective of the studies dealing with

the allocation of computation resources is to minimize the

execution delay of the offloaded application (D). In other
words the aim is to ensure QoS to the UEs in order to fully

exploit proximity of the MEC with respect to the computing

in faraway CC. Moreover, several studies also focus on mini-

mization of the energy consumption of computing nodes (EC ).
In addition, some limited effort has been focused on balancing

of computing and communication load to more easily satisfy

the requirements on execution delay and/or to minimize overall

resources utilization.

A common drawback of all proposed solutions is that only

simulations are provided to demonstrate proposed solutions

for allocation of MEC computing resources. Moreover, all

papers disregard mobility of the UEs. Of course, if the UEs

are fixed, individual proposal yield a satisfactory execution

delay and/or power consumption at

the computing nodes.

Nevertheless, if the UE moves far away from the computing

nodes, this could result in significant QoS degradation due to

long transmission latency and extensive users dissatisfaction.

This issue is addressed in the subsequent section targeting

mobility management for the MEC.

VII. MOBILITY MANAGEMENT FOR MEC

In the conventional mobile cellular networks, a mobility of

users is enabled by handover procedure when the UE changes

the serving eNB/SCeNB as it roams throughout the network

to guarantee the service continuity and QoS. Analogously,

if the UE offloads computation to the MEC, it is important

to ensure the service continuity. In fact, there are several

options how to cope with the mobility of UEs. The first option,

applicable only for the UEs with a low mobility (e.g. within

a room), is to adapt transmission power of the eNB/SCeNB

during the time when the offloaded application is processed

by the MEC (Section VII-A). If the UE performs handover

to the new serving eNB/SCeNB despite of the power control,

the service continuity may be guarantee either by the VM

migration (i.e., the process during which the VM run at

the current computing node(s) is migrated to another, more

suitable, computing node(s) as discussed in Section VII-B) or

by selection of a new communication path between the UE

and the computing node (Section VII-C).

A. Power control

In case when the UEs’ mobility is low and limited, e.g.,

when the UEs are slowly moving inside a building, a proper

setting of the transmission power of the serving and/or neigh-

boring SCeNBs can help to guarantee QoS. This is considered

in [108], where the authors propose a cloud-aware power

control (CaPC) algorithm helping to manage the offloading

of real-time applications with strict delay requirements. The

main objective of the CaPC is to maximize the amount of

the offloaded applications processed by the MEC with a given

latency constrain. This is achieved by an adaptation of the

transmission power of the SCeNBs so that the handover to a

new SCeNB is avoided if possible (see the basic principle

in Fig.19 where the moving UE remains connected to the

same SCeNB as its transmission power is increased). The

CaPC is composed of coarse and fine settings of the SCeNBs

transmission power. The purpose of the coarse setting is to

find an optimal default transmission power Pt,def , which is
applied if all of the UEs attached to the SCeNB are idle.

Setting of the Pt,def depends on the power level received
by the serving SCeNB from the most interfering neighboring

SCeNB and the interference generated by the eNBs. The fine

setting consists in a short-term adaptation of the SCeNB’s

transmission power when the UE would not be able to receive

the offloaded application from the cloud due to low SINR.

If the CaPC is utilized, up to 95% applications computed at

the SCeNBSs are successfully delivered back to the UE with

satisfying delay. Contrary, a conventional, non-cloud-aware,

power control is able to successfully deliver only roughly 80%

of offloaded applications.

The main disadvantage of the CaPC presented in [109] is

that the time when the fine adjustment of the transmission

Fig. 19: Principle of CaPC according to [108][109].

power is triggered (∆t) is the same for all SCeNBs and UEs
independently on the channel quality (i.e., SINR). As a con-

sequence, the CaPC may be triggered too late when sufficient

SINR cannot be guaranteed in due time to successfully deliver

the offloaded application back to the UE. This problem is

addressed in [109], where the ∆t is set individually for each
UEs depending on its current channel quality. The proposed

algorithm finds ∆t by iterative process when ∆t is adapted
after each application is successfully delivered back to the UE.

This way, the amount of successfully delivered applications is

increased up to 98%, as demonstrated by simulations.

B. VM migration

If the UEs mobility is not limited, as considered in Sec-

tion VII-A, and power control is no longer sufficient to keep

the UE at the same serving eNB/SCeNB, a possibility to

initiate the VM migration should be contemplated in order to

guarantee the service continuity and QoS requirements. On one

hand, the VM migration has its cost (CostM ) representing the
time required for the VM migration and backhaul resources

spent by transmission of the VM(s) between the computing

nodes. On the other hand, there is a gain if the VM migration is

initiated (GainM ) since the UE can experience lower latency
(data is processed in UE’s vicinity) and backhaul resources do

not have to be allocated for transmission of the computation

results back to the UE.

A preliminary analysis how the VM migration influences

performance of the UE is tackled in [110]. The authors

describe analytical model based on Markov chains. Without

the VM migration, a probability that the UE is connected to

the optimal MEC decreases with increasing number of hops

between the UE and the eNB, where the service is initially

placed. This also results in increasing delay. Contrary, the

connection of the UE to the optimal MEC server results in

the lowest delay but at the high cost of the migration. The

reason for this phenomenon is that the VM migration should

be ideally initiated after each handover performed by the UE

to keep minimum delay.

While the previous paper is more general and focused on

preliminary analysis regarding the VM migration, the main

objective of [111] is to design a proper decision policy

determining whether to initiate the VM migration or not. As

discussed above, there is a trade-off between the migration

Fig. 20: VM migration principle according to [111].

cost (CostM ) and migration gain (GainM ). The authors
formulate the VM migration policy as a Continuous Time

MDP (CTMDP) and they try to find an optimal threshold

policy when the VM migration is initiated. Consequently, after

each handover to the new eNB, the optimal threshold policy

decides whether the VM migration should be initiated or not.

An example of this principle is shown in Fig. 20, where

the UE exploiting the MEC1 moves from the eNB1 to the

eNBn. While the conventional radio handover is performed

whenever the UE crosses cell boundaries, the VM migration

is initiated after handover to the eNBn is performed since

CostM < GainM . Simulations show, that the proposed optimal policy always achieves the maximum expected gain

if compared to never migrate strategy (i.e., the computation is

located still at the same MEC) and the scheme when the VM

migration is performed after a specific number of handovers

(10 handovers is set in the paper).

A proper trade-off between VM migration cost (CostM )
and VM migration gain (GainM ) is also studied in [112].
The paper proposes a Profit Maximization Avatar Placement

(PRIMAL) strategy deciding whether the VM should be

migrated or not. Since the PRIMAL problem is NP-hard,

the authors use Mixed-Integer Quadratic Programming tool

to find the heuristic solution. The proposed solution is able

to significantly reduce execution delay when compared to the

situation with no migration (roughly by 90%) while reducing

the migration cost approximately by 40%. When compared

to [111], the authors also show the influence of α parameter
weighing CostM and GainM . Basically, with increasing α,
the migration cost is decreasing (i.e., migration is not done so

frequently), but at the cost of higher execution delay.

An optimal threshold policy for the VM migration is also

considered in [113]. The problem is again formulated as the

MDP and the VM migration is initiated always if the state of

the UE is bounded by a particular set of thresholds. The state

of the UE is defined as the number of hops between the eNB to

which the UE is connected and the location of the MEC server

where the computing service is running (in the paper labelled

as the offset). The main objective of the paper is to minimize

the overall sum cost by the optimal VM migration decision

(i.e., the VM migration is performed if CostM < GainM as explained earlier). The authors proof the existence of the

optimal threshold policy and propose an iterative algorithm in

order to find the optimal thresholds for the VM migration. The

time complexity of the algorithm is O(|M|N), where M and
N is the maximum negative and positive offset, respectively.
The performed results proof the optimal threshold policy is

able to always outperform ”never migrate” or ”always migrate”

strategies in terms of the sum cost.

The main drawback of [111][113] is that these assume

simple 1D mobility model. More general setting for the VM

migration is contemplated in [114], where 2D mobility and

real mobility traces are assumed. The authors formulate a

sequential decision making problem for the VM migration

using MDP and define algorithm for finding optimal policy

with the complexity O(N3), where N is the number of states
(note that the state is defined as the number of hops between

the eNB to which the UE is connected and the location of

the MEC server analogously to [113]). Since the proposed

optimal VM migration strategy is too complex, the authors

propose an approximation of the underlying state space by

defining the space as a distance between the UE and the

MEC server where the service is running. In this case, the

time complexity is reduced to O(N2). As demonstrated by
the numerical evaluations, the proposed migration strategy is

able to decrease sum cost by roughly 35% compared to both

never and always migrate strategy.

The VM migration process may be further improved by a

mobility prediction as demonstrated in [115]. The proposed

scheme is able to: 1) estimate in advance a throughput that

user can receive from individual MEC servers as it roams

throughout the network, 2) estimate time windows when the

user perform handover, and 3) and VM migration manage-

ment scheme selecting the optimal MEC servers according to

offered throughput. The simulation results demonstrate that

the proposed scheme is able to decrease latency by 35%

with respect to scheme proposed in [111]. Nonetheless, a

disadvantage of the proposal is that it requires huge amount

of information in order to predict the throughput. Moreover,

the paper does not consider the cost of migration itself.

In [116], the VM migration decision process is further

enhanced by the mechanism predicting future migration cost

with specified upper bound on a prediction error. The main

objective of the paper is, similarly as in [113][114], to

minimize the sum cost over a given time. First, the authors

propose an offline algorithm for finding the optimal placement

sequence for a specific look-ahead window size T , which
represents the time to which the cost prediction is done.

For the offline algorithm, an arrival and a departure of the

applications offloaded to the MEC are assumed to be exactly

known. The time complexity of the algorithm is O(M2T ),
where M stands for the number of MEC serves in the system.
The VM migration is strongly dependent on the size of T . If
T is too large, the future predicted values may be far away
from the actual values and, thus, the VM migration far from

the optimal. Contrary if T is too short, a long term effect of
the service placement is not considered. As a result, also a

binary search algorithm finding the optimal window size is

proposed in the paper. The proposed offline algorithm is able

to reduce cost by 25% (compared to never migrate strategy)

and by 32% (compared to always migrate strategy). Although

the simulation results are demonstrated for the multi-UEs

scenario, the problem is formulated only for the single-UE.

Hence, the paper is further extended in [117] for the multi-

UEs offloading K applications to the MEC. Similarly as in
[116], the problem is solved by the offline algorithm with

complexity of O(MK2T ). Since the offline algorithm is of
high complexity and impractical for real systems, the paper

also propose an online approximation algorithm reducing the

complexity to O(M2KT ). The proposed online algorithm
outperforms never migrate and always migrate strategies by

approximately 32% and 50%, respectively.

So far, all the studies focusing on the VM migration do

not consider an impact on a workload scheduling, i.e., how

the VM migration would be affected by a load of individual

MEC servers. As suggested in [118], the problem of the VM

migration and scheduling of the MEC workloads should be

done jointly. Although the problem could be formulated as

a sequential decision making problem in the framework of

MDPs (like in above studies) it would suffer from several

drawbacks, such as, 1) extensive knowledge of the statistics of

the users mobility and request arrival process is impractical,

2) problem can is computationally challenging, and 3) any

change in the mobility and arrival statistics would require re-

computing the optimal solution. Hence, the main contribution

of [118] is a development of a new methodology overcoming

these drawbacks inspired by Lyapunov optimization frame-

work. The authors propose online control algorithm making

decision on where the application should be migrated so that

the overall transmission and reconfiguration costs are mini-

mized. The complexity of the algorithm is O(M!/(M −K)!),
where M is the number of MEC servers and K is the amount
of applications host by the MEC. By means of proposed

optimization framework, the reconfiguration cost is reduced

when compared to always migrate strategy (by 7%) and never

migrate strategy (by 26%).

While the main objective of the previous papers focusing

on the VM migration is to make a proper decision on whether

to migrate or not, the main aim of the authors in [119] is

to minimize the VM migration time when the migration is

about to be performed. This is accomplished by a compression

algorithm reducing the amount of transmission data during the

migration itself. On one hand, if the compression rate of the

algorithm is low, more data has to be transmitted, but the com-

pression itself is shorter in terms of time. On the other hand, a

higher compression rate results in a significant reduction of the

transmitted data during the VM migration, but the compression

takes significant amount of time. Hence, the paper proposes

a dynamic adaptation of the compression rate depending on

the current backhaul available bandwidth and the processing

load of the MEC. The paper presents extensive experiments

on real system showing that the dynamic adaptation during

the VM migration is able to cope with changing of available

bandwidth capacity.

A proper VM migration may not result only in an execution

delay reduction, but it can also increase throughput of the sys-

tem as demonstrated in [120]. The paper proposes a protocol

architecture for cloud access optimization (PACAO), which is

based on Locator/Identifier Separation Protocol (LISP) [121].

If the user is experiencing latency or jitter above maximum

tolerated threshold, the VM migration to a new MEC server is

initiated. The selection of the new MEC server, which is about

to host VM of the user is based on the required computing

power and availability of resources at the MEC servers. The

proposal is evaluated by means of both experiments on real

testbed and simulations. The results show that the system

throughput is increased by up to 40% when compared to the

case without VM migration.

C. Path selection and/or VM migration

The VM migration is not a convenient option when a huge

amount of data needs to be migrated among the computing

nodes and the whole process may take minutes or even hours

[119]. Even if the migration process lasts few seconds, real-

time applications cannot be offloaded to the MEC. Moreover,

the load imposed on backhaul links may be too significant.

In such cases, finding and optimizing new paths for delivery

of the computed data from the MEC are a more viable

option. This eventuality is considered in [122], where the path

selection algorithm for a delivery of the offloaded data from

the cluster of computing SCeNBs to the UE is proposed. The

main objective of the path selection algorithm is to minimize

transmission delay taking into account quality of both radio

and backhaul links. Moreover, the authors enable to enforce

handover to new serving SCeNB to minimize the transmission

delay. An example of the data delivery is shown in Fig. 21,

where three SCeNBs are computing the application offloaded

by the UE. The data processed by the serving SCeNB2 are

received by the UE directly while data from the SCeNB3 are

delivered to the UE through the CN and the serving SCeNB2.

Finally, the UE performs handover to the SCeNB1 and, then, it

receives results from the SCeNB3 directly via radio link. The

complexity of the proposed algorithm is O(mn), where m is
the number of UEs and n the amount of the SCeNBs in cluster.
The proposed algorithm is able to reduce transmission delay

by up to 9% with respect to a case when the UE receives all

data from the same serving SCeNB. In [123], the algorithm’s

complexity is further decreased to O(In), where I is the set
of SCeNBs with sufficient radio/backhaul link quality. It is

Fig. 21: An example of path selection algorithm proposed

in [122] (cRx and C
B
x stands for capacity of radio links and

backhaul links, respectively).

TABLE V: The comparison of individual papers focusing on mobility management in MEC.

Mobility

man.
method

Objective Proposed method Mobility

model

Evaluation
method

Results Algorithm

complex-
ity

[108] Power
control

1) Maximize the amount
of delivered requests
from the MEC, 2)
Guaranteeing latency
constraints

Adaptation of transmission
power of SCeNBs

2D limited
mobility (e.g.,
apartment)

Simulations Up to 95% offloaded
applications successfully
delivered

[109] Power
control

1) Maximize the amount
of delivered requests
from the MEC, 2)
Guaranteeing latency
constraints

Adaptation of transmission
power of SCeNBs, optimiza-
tion of power control trigger
time

2D limited
mobility (e.g.,
apartment)

Simulations Up to 98% offloaded
applications successfully
delivered

[110] VM mi-
gration

1) Define analytical
model for VM
migration, 2) Analyze
how VM migration
influences e2e delay

– 2D random
walk model

Analytical
model

N/A –

[111] VM mi-
gration

1) Maximize total ex-
pected reward

Formulation of an optimal
threshold decision policy ex-
ploiting MDP

1D random
walk

Simulations Always maximize total
expected reward wrt al-
ways/never migrate

[112] VM mi-
gration

1) Find a trade-off
between CostM and
GainM

Profit Maximization Avatar
Placement (PRIMAL) strat-
egy deciding whether VM
should be migrated or not

Random way
point model

Simulations Reducing execution de-
lay by 90% wrt no mi-
gration, reducing migra-
tion cost by 40% wrt to
always migrate

[113] VM mi-
gration

1) Minimize system sum
cost over a given time

Formulation of an optimal
threshold decision policy us-
ing MDP

1D asymmetric
random walk
mobility model

Simulations Always minimize over-
all cost wrt always/never
migrate

O(|M|N)

[114] VM mi-
gration

1) Minimize system sum
cost over a given time

Formulation of an optimal
sequential decision policy
using MDP

2D mobility,
real mobility
traces

Simulations 30% reduction of
average cost wrt to
never/always migrate

O(N2)

[115] VM mi-
gration

1) Minimize execution
delay

Estimation of throughput of-
fered by MEC servers based
on mobility prediction

Cars moving
by a predefined
paths

Simulations Reducing latency by
35% wrt [111]

[116] VM mi-
gration

1) Minimize system sum
cost over a given time

Offline algorithm for find-
ing optimal placement se-
quence for a specific look-
ahead window size

Real world user
mobility traces

Simulations 25%(32%) red. of
average cost wrt to
never(always) migrate

O(M2T)

[117] VM mi-
gration

1) Minimize system sum
cost over a given time

Offline and online algorithms
for finding optimal place-
ment sequence for a specific
look-ahead window size

Real world user
mobility traces

Analytical,
simulations

32%(50%) red. of
average cost wrt to
never(always) migrate

O(MK2T)

[118] VM mi-
gration

1) Minimize overall
transmission and
reconfiguration costs

Online control algorithm
making decision where
application should be placed
and migrated

1) Random
walk, 2) Real
world user
mobility traces

Analytical
evalu-
ations,
simulations

7%(26%) red. of re-
configuration cost wrt to
never(always) migrate

O(M!/(M−
K)!) for
M ≥ K

[119] VM mi-
gration

1) Minimize VM migra-
tion time

Adaptation of compression
rate during VM migration
depending on available band-
width and processing load

– Experiments
on real
system

N/A –

[120] VM mi-
gration

1) Maximize throughput Protocol architecture for
cloud access optimization
exploiting LISP

Real world user
mobility traces

Experiments
on testbed,
simulations

Increase of throughput
up to 40%

[122] Path se-
lection

1) Minimize transmis-
sion delay

Path selection exploiting
handover mechanism

Manhattan mo-
bility model

Simulations Reduction of transmis-
sion delay by up to 9%

O(mn)

[123] Path se-
lection

1) Minimize transmis-
sion delay
Path selection exploiting
handover mechanism
Manhattan mo-
bility model

Simulations Reduction of transmis-
sion delay by up to 54%

O(Zn)

[124] Path se-
lection +
VM mi-
gration

1) Minimize transmis-
sion delay

Cooperative service migra-
ton and path selection algo-
rithm with movement predic-
tion

Smooth random
mobility model

Simulations Reduction of transmis-
sion delay by up to 10%
wrt [123]

O(|Z||I|τ),
O(|I|τ)

shown that the proposed path selection algorithm is able to

reduce transmission delay by 54%.

The path selection algorithm contemplated in [122][123]

may not be sufficient if the UE is too far away from the

computing location since increased transmission delay may

result in QoS reduction notwithstanding. Hence, the authors

in [124] suggest a cooperation between an algorithm for

the dynamic VM migration and the path selection algorithm

proposed in [123] further enhanced by consideration of a

mobility prediction. The first algorithm decides whether the

VM migration should be initiated or not based on the mobility

prediction and the computation/communication load of the

eNB(s). The second algorithm, then, finds the most suitable

route for downloading the offloaded data with the mobility

prediction outcomes taken into account. The complexity of the

first algorithm is O(|Z||I|τ) and the complexity of the second
algorithm equals to O(|I|τ), where Z is the number of eNBs
with sufficient channel quality and computing capacity, and τ
stands for the size of the prediction window. The proposed

algorithm is able reducing the average offloading time by

27% comparing to the situation when the VM migration is

performed after each conventional handover and by roughly

10% with respect to [123].

D. Summary of works focused on mobility management

A comparison of the studies addressing the mobility issues

for the MEC is shown in Table V. As it can be observed

from Table V, the majority of works so far focuses on the

VM migration. Basically, the related papers try to find an

optimal decision policy whether the VM migration should

be initiated or not to minimize overall system cost (up to

32% and up to 50% reduction of average cost is achieved

compared to never and always migrate options, respectively

[117]). Moreover, some papers aim to find a proper trade-off

between VM migration cost and VM migration gain [112],

minimizing execution delay [115], minimizing VM migration

time [119], or maximizing overall throughput [120].

From Table V can be further observed that all papers dealing

with the VM migration assume the computation is done by a

single computing node. Although this option is less complex,

the parallel computation by more nodes should not be entirely

neglected as most of the papers focusing on the allocation of

computing resources assume multiple computing nodes (see

Section VI-B).

VIII. LESSONS LEARNED

This section summarizes lessons learned from the state of

the art focusing on computation offloading into the MEC. We

again address all three key items: decision on computation

offloading, allocation of computing resources, and mobility

management.

From the surveyed papers dealing with the decision on com-

putation offloading, following key observations are derived:

• If the channel quality between the UE and its serving

station is low, it is profitable to compute rather locally

[95]. The main reason is that the energy spent by the trans-

mission/reception of the offloaded data is too expensive

in terms of the energy consumption at the UE. Contrary,

with increasing quality of the channel, it is better to

delegate the computation to the MEC since the energy

cost required for transmission/reception of the offloaded

data is reduced and it is easily outweighed by the energy

saving due to the remote computation. Consequently, the

computation can be offloaded more frequently if MIMO

is exploited as it improves channel quality. Moreover, it

is efficient to exploit connection through SCeNBs for

the offloading as the SCeNBs are supposed to serve fewer

users in proximity providing high channel quality and more

available radio resources.

• The most suitable applications for offloading are those

requiring high computational power (i.e., high compu-

tational demanding applications) and, at the same time,

sending only small amount of data [82]. The reason is that

the energy spent by transmission/reception of the offloaded

computing is small while the energy savings achieved by

the computation offloading are significant. Contrary, the

applications that need to offload a lot of data should

be computed locally as the offloading simply does not pay

off due to huge amount of energy spent by the offloading

and high offloading delays.

• If the computing capacities at the MEC are fairly

limited, the probability to offload data for processing

is lowered. This is due to the fact that the probabilities of

the offloading and local processing are closely related to

the computation power available at the MEC.

• With more UEs in the system, the application offloading

as well as its processing at the MEC last longer [96].

Consequently, if there is high amount of UEs in the system,

the local processing may be more profitable, especially if

the minimization of execution delay is the priority (such is

the case of real-time applications).

• The energy savings achieved by the computation offload-

ing is strongly related to the radio access technology

used at radio link. To be more specific, OFDMA enables

significantly higher energy savings of the UEs than TDMA

due to higher granularity of radio resources [92].

• The partial offloading can save significantly more energy

at the UE when compared to the full offloading [72].

Nevertheless, in order to perform the partial offloading,

the application has to enable parallelization/partitioning.

Hence, the energy savings accomplished by computation

offloading is also strongly related to the application type

and the way how the code of the application is written.

From the surveyed papers focused on allocation of comput-

ing resources, the following key facts are learned:

• The allocation of computation resources is strongly

related to the type of the application being offloaded

in a sense that only applications allowing paralleliza-

tion/partitioning may be distributed to multiple computing

nodes. Obviously, a proper parallelization and code

partitioning of the offloaded application can result in

shorter execution delays as multiple nodes may pool their

computing resources (up to 90% reduction of execution

delay when compared to single computing node). On the

other hand, the allocation of computation resources for

parallelized applications is significantly more complex.

• An increase in the number of computing nodes does

not have to result always in a reduction in the execu-

tion delay [102]. On the contrary, if the communication

delay becomes predominant over the computation delay,

the overall execution delay may be even increased. Hence,

a proper trade-off between the number of computing nodes

and execution delay needs to be carefully considered when

allocating computing resources to offloaded data.

• If the backhaul is of a low quality, it is mostly preferred

to perform the computation locally by the serving

node (e.g., SCeNB/eNB) since the distribution of data for

computing is too costly in terms of the transmission latency.

Contrary, a high quality backhaul is a prerequisite for

an efficient offloading to multiple computing nodes.

• The execution delay of the offloaded application de-

pends not only on the backhaul quality, but also on a

backhaul topology (e.g., mesh, ring, tree, etc.) [102]. The

mesh topology is the most advantageous in terms of the

execution delay since all computing nodes are connected

directly and distribution of the offloaded data for computing

is more convenient. On the other hand, mesh topology

would require huge investment in the backhaul.

Finally, after surveying the papers addressing mobility is-

sues in the MEC, we list following key findings:

• There are several options of the UE’s mobility management

if the data/application is offloaded to the MEC. In cases of

the low mobility, the power control at the SCeNBs/eNBs

side can be sufficient to handle mobility (up to 98% of

offloaded applications can be successfully delivered back

to the UE [109]). This is true as long as the adaption of

transmission power enables keeping the UE at the same

serving station during the computation offloading. However,

if the UE performs handover, the power control alone is not

sufficient and the VM migration or new communication

path selection may be necessary to comply with require-

ments of offloaded applications in terms of latency.

• A decision on VM migration depends strongly on three

metrics:

1) The VM migration cost (CostM ) representing the time
required for the service migration and the backhaul

resources spent by the transmission of VM(s) between

the computing nodes.

2) The VM migration gain (GainM ) is the gain consti-
tuting delay reduction (data are computed in proximity

of the UE) and saving of the backhaul resources (data

does not have to be sent through several nodes).

3) The computing load of the node(s) to which the VM is

reallocated since, in some situations, the optimal com-

puting node for the VM migration may be unavailable

due to its high computation load.

• The VM migration is impractical if huge amount of data

needs to be transmitted between the computing nodes

and/or if the backhaul resources between VMs are inad-

equate since it may take minutes or even hours to migrate

whole VM. This is obviously too long for real-time services

and it also implies significant load on backhaul, especially

if the VM migration would need to be performed frequently.

Note that time consuming migration goes against the major

benefit of the MEC, i.e., low latency resulting in suitability

of the offloading for real-time services.

• The minimization of the VM migration time can be

done by reduction of the amount of migrated data [119].

Nonetheless, even this option is not enough for real-time

services. Thus, various path selection algorithms should

be employed with purpose to find the optimal path for

delivery of the offloaded data back to the UEs while

computing is done by the same node(s) (i.e., without VM

migration) [123]. However, if the UE moves too far away

from the computation placement, more robust mobility

management based on joint VM migration and path

selection should be adopted [124].

IX. OPEN RESEARCH CHALLENGES AND FUTURE WORK

As shown in the previous sections, the MEC has attracted a

lot of attention in recent years due to its ability to significantly

reduce energy consumption of the UEs while, at the same time,

enabling real-time application offloading because of proximity

of computing resources to the users. Despite this fact the

MEC is still rather immature technology and there are many

challenges that need to be addressed before its implementation

into mobile network to be beneficial. This section discusses

several open research challenges not addressed by the current

researcher.

A. Distribution and management of MEC resources

In Section III, we have discussed several possible options for

placement of the computing nodes enabling the MEC within

the mobile network architecture. To guarantee ubiquitous

MEC services for all users wanting to utilize the MEC, the

MEC servers and the computation/storage resource should

be distributed throughout whole network. Consequently, the

individual options where to physically place the MEC servers

should complement each other in a hierarchical way. This

will allow efficient usage of the computing resources while

respecting QoS and QoE requirements of the users. In this

context, an important challenge is to find an optimal way

where to physically place the computation depending on

expected users demands while, at the same time, consider

related CAPEX and OPEX (as initially tackled in [67][68]).

Another missing topic in the literature is a design of

efficient control procedures for proper management of the

MEC resources. This includes design of signalling messages,

their exchange and optimization in terms of signalling over-

head. The control messages should be able to deliver status

information, such as load of individual computing nodes

and quality of wireless/backhaul links in order to efficiently

orchestrate computing resources within the MEC. There is a

trade-off between high signalling overhead related to frequent

exchange of the status information and an impact on the MEC

performance due to aging of the status information if these are

exchanged rarely. This trade-off have to be carefully analysed

and efficient signalling mechanisms need to be proposed to

ensure that the control entities in the MEC have up to date

information at their disposal while the cost to obtain them is

minimized.

B. Offloading decision

The offloading decision plays a crucial part as it basically

determines whether the computation would be performed

locally, remotely or jointly in both locations as discussed in

Section V. All papers focusing on the offloading decision

consider only the energy consumption at the side of the UE.

However, to be in line with future green networking, also

the energy consumption at the MEC (including computation

as well as related communication) should be further taken

into account during the decision. Moreover, all papers dealing

with the offloading decision assume strictly static scenarios,

i.e., the UEs are not moving before and during the offload-

ing. Nevertheless, the energy necessary for transmission of

the offloaded data can be significantly changed even during

offloading if channel quality drops due to low movement or

fading. This can result in the situation when the offloading may

actually increase the energy consumption and/or execution

delay comparing to local computation. Hence, it is necessary

to propose new advanced methods for the offloading decision,

for instance, exploiting various prediction techniques on the

UEs mobility and channel quality during the offloading to

better estimate how much the offloading will cost for varying

conditions.

Besides, current papers focusing on the partial offloading

decision disregard the option to offload individual parts to

multiple computing nodes. Multiple computing nodes enables

higher flexibility and increases a probability that the offloading

to the MEC will be efficient for the UE (in terms of both

energy consumption and execution delay). Of course, a sig-

nificant challenge in this scenario belongs to consideration of

backhaul between the MEC servers and ability to reflect their

varying load and parameters during the offloading decision.

C. Allocation of computing resources

The studies addressing the problem of an efficient allocation

of the computing resources for the application offloaded to

the MEC do not consider dynamicity of the network. To be

more precise, the computing nodes (e.g., SCeNBs, eNB) are

selected in advance before the application is offloaded to the

MEC and then the same computing node(s) is (are) assumed

to process the offloaded application (at least as long as the UE

is relatively static and does not perform handover among cells

as considered in Section VII). However, if some additional

computing resources are freed while given application is

processed at the MEC, these resources could be also allocated

for it in order to farther speed up the offloaded computing.

Hence, a dynamic allocation of the computing resources during

processing of the offloaded applications in the MEC is an

interesting research challenge to be addressed in the future.

So far all the studies focusing on the allocation of computing

resources assume a ”flat” MEC architecture in a sense that

the MEC computing nodes are equally distributed and of the

same computing power. In this respect, it would be interesting

to consider more hierarchical placement of the computing

nodes within the MEC. More specifically, computing resources

should be distributed within the network as described in Sec-

tion III-B3 (e.g., cluster of SCeNBs, eNBs, aggregation points

or even at the edge of CN). A hierarchical MEC placement

should result in a better distribution of the computing load and

a lower execution delay experienced by the users since the use

of distant CC can be more easily avoided.

D. Mobility management

So far, the works focusing on mobility management and

particularly on the VM migration consider mostly a scenario

when only a single computing node (SCeNB or eNB) makes

computation for each UE. Hence, the challenge is how to

efficiently handle the VM migration procedure when appli-

cation is offloaded to several computing nodes. Moreover,

the VM migration impose high load on the backhaul and

leads to high delay, which makes it unsuitable for real-

time applications. Hence, new advanced techniques enabling

very fast VM migration in order of milliseconds should be

developed. However, this alternative is very challenging due

to communication limits between computing nodes. Therefore,

more realistic challenge is how to pre-migrate the computation

in advance (e.g., based on some prediction techniques) so that

there would be no service disruption observed by the users.

Despite of above-mentioned suggestions potentially reduc-

ing VM migration time, stand-alone VM migration may be

unsuitable for real-time applications notwithstanding. Conse-

quently, it is important to aim majority of research effort

towards a cooperation of the individual techniques for mobility

management. In this regard, dynamic optimization and joint

consideration of all techniques (such as power control, VM

migration, compression of migrated data, and/or path selec-

tion) should be studied more closely in order to enhance QoE

for the UEs and to optimize overall system performance for

moving users.

E. Traffic paradigm imposed by coexistence of offloaded data

and conventional data

Current research dealing with the decision on computation

offloading, allocation of computing resources and mobility

management mostly neglects the fact that conventional data

not offloaded to the MEC, such as VoIP, HTTP, FTP, machine

type communication, video streaming, etc., has to be transmit-

ted over radio and backhaul links in parallel to the offloaded

data. Hence, whenever any application is being offloaded to

the MEC, it is necessary to jointly allocate/schedule commu-

nication resources both for the offloaded data to the MEC

and the conventional data (i.e., data not exploiting MEC) in

order to guarantee QoS and QoE. Especially, if we consider

the fact that the offloaded data represents additional load

on already resource starving mobile cellular networks. The

efficient scheduling of the communication resources may also

increase the amount of data offloaded to the MEC because of

more efficient utilization radio and backhaul communication

links.

Besides, the offloading reshapes conventional perception of

uplink/downlink utilization as the offloading is often more

demanding in terms of the uplink transmission (offloading

from the UE to the MEC). The reason for this is that many ap-

plications require delivering of large files/data to the MEC for

processing (e.g., image/video/voice recognition, file scanning,

etc.) while the results delivered to the UE are of significantly

lower volume. This paradigm motivates for rethinking and

reshaping research effort from sole downlink to the mixed

downlink and uplink in the future.

F. Concept validation

As shown in Section V, VI, and VII, the MEC concept

is analyzed and novel algorithms and proposed solutions are

validated typically by numerical analysis or by simulations. In

addition, majority of work assume rather simple, and some-

times unrealistic, scenarios for simplification of the problem.

Although these are a good starting point in uncovering MEC

potentials, it is important to validate key principles and find-

ings by means of simulations under more complex and realistic

situations and scenarios such as, e.g., in [114]-[118] where

at least real world user mobility traces are considered for

evaluation and proposals on VM migration. At the same time,

massive trials and further experiments in emulated networks

(like initially provided in [42]) or real networks (similar to

those just recently performed by Nokia [46]) are mandatory

to move the MEC concept closer to the reality.

X. CONCLUSION

The MEC concept brings computation resources close to

the UEs, i.e., to the edge of mobile network. This enables

to offload highly demanding computations to the MEC in

order to cope with stringent requirements of applications on

latency (e.g., real time applications) and to reduce energy

consumption at the UE. Although the research on the MEC

gains its momentum, as reflected in this survey after all, the

MEC itself is still immature and highly unproved technology.

In this regard, the MEC paradigm introduces several critical

challenges waiting to be addressed to the full satisfaction of all

involved parties such as mobile operators, service providers,

and users. The alpha and the omega of current research

regarding the MEC is how to guarantee service continuity in

highly dynamic scenarios. This part is lacking in terms of

research and is one of the blocking point to enroll the MEC

concept. Moreover, recent research validates solution mostly

under very simplistic scenarios and by means of simulations

or analytical evaluations. Nevertheless, to demonstrate the

expected values introduced by the MEC, real tests and trials

under more realistic assumptions are further required.

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  • I Introduction
  • II Use cases and service scenarios
  • II-A Consumer-oriented services
    II-B Operator and third party services
    II-C Network performance and QoE improvement services

  • III MEC Architecture and standardization
  • III-A Overview of the MEC concept
    III-A1 Small cell cloud (SCC)
    III-A2 Mobile micro clouds (MMC)
    III-A3 Fast moving personal cloud (MobiScud)
    III-A4 Follow me cloud (FMC)
    III-A5 CONCERT
    III-B ETSI MEC
    III-B1 Standardization of ETSI MEC
    III-B2 ETSI MEC reference architecture
    III-B3 Deployment options of ETSI MEC
    III-C Summary

  • IV Introduction to computation offloading
  • V Decision on computation offloading to MEC
  • V-A Full offloading
    V-A1 Minimization of execution delay
    V-A2 Minimization of energy consumption while satisfying execution delay constraint
    V-A3 Trade-off between energy consumption and execution delay
    V-B Partial offloading
    V-B1 Minimization of energy consumption while satisfying execution delay constraint
    V-B2 Trade-off between energy consumption and execution delay
    V-C Summary of works focusing on computation offloading decision

  • VI Allocation of computing resources
  • VI-A Allocation of computation resources at a single node
    VI-B Allocation of computation resources at multiple nodes (federated clouds)
    VI-B1 Minimization of execution delay and/or power consumption of computing nodes
    VI-B2 Balancing of communication and computation load
    VI-C Summary of works dealing with allocation of computing resources

  • VII Mobility management for MEC
  • VII-A Power control
    VII-B VM migration
    VII-C Path selection and/or VM migration
    VII-D Summary of works focused on mobility management

  • VIII Lessons learned
  • IX Open research challenges and future work
  • IX-A Distribution and management of MEC resources
    IX-B Offloading decision
    IX-C Allocation of computing resources
    IX-D Mobility management
    IX-E Traffic paradigm imposed by coexistence of offloaded data and conventional data
    IX-F Concept validation

  • X Conclusion
  • References

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