Page 21 - ITU Journal Future and evolving technologies – Volume 2 (2021), Issue 2

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 2

on its communication perspective including joint radio 4.1 Fog architecture‑based solutions

and computational resource management. Hence, the
for seamless handover
proposed work equally highlighted interesting topics on
MEC such as deployment of edge cloud systems,

Wan et al. [28] proposed a novel fog computing

cache‑enabled MEC, management of mobility for edge

archi‑ tecture that includes new schemes and

cloud, and privacy aware edge cloud. In addition a mobile techniques for symmetric inter‑ ile coded cache

computing platform for 5G is presented, as well as the
placement, handling the inter‑SBS communication

comparison between MCC and MEC, MEC computation

phase, and a new asymmetric and optimal cache

and communication models, resource management in

placement that performs ile sub packaging according

MEC and a list of issues and research directions. to the network structure.
Issues and challenges encountered in MCC computation

loading have been highlighted by i et al. [26].
Fog computing is known as a favorable

Hence, they presented state‑of‑the‑art data of loading

architecture for computing and resource management

techniques, computation loading methods, and an

that provides cloud services closer to end users, that is

analysis of the techniques along with their principal

at the network edge. It includes both the data plane

issues. They additionally explored the major parameters

and control plane and aids applications in the area of

on which the frameworks are based and implemented

IoT, in 5G systems and arti icial intelligence. Fog

such as of loading method and grade of partitioning. In

computing reduces the need for specialized

addition, the MCC computation of loading was de ined

applications deployed just for the cloud, endpoints or

as the task of sending computation intensive application

edge devices, by enabling the same application to run

components to a remote server, which handles and

anywhere and allowing applications from different

executes the computational tasks. Different of loading

suppliers to run on the same hardware without

approaches, framework mechanisms and classes were interference [29].
also presented along with an insightful comparison of the

frameworks for computational loading. Some of the

Luan et al. [30] outlined the main features of fog

approaches that were presented in the paper use static

computing and described its concept, architecture and

loading unlike others that utilize dynamic of loading.

design goals in an article. Fog computing is an architec‑
However, all the techniques were aimed to improve

ture that enables the deployment of virtualized cloud‑like

the potentialities of mobile devices by saving energy,

devices closer to mobile users. Edge computing is a dis‑

reducing response time, or minimizing the execution

tributed computing paradigm that enables cloud services
cost.
closer to the location where it is needed, it extends cloud
In the same perspective, Shakarami et al. [27] proposed
abilities at the edge of the computing network to execute
a survey of the stochastic‑based computation of loading
high‑demanding computational tasks and save a very

approaches in MCC environments including a taxon‑ omy

signi icant amount of data at the surroundings of user
of the techniques categorized into three ields, which are

equipment [6]. Communication between fog nodes is
Markov process, Markov chain, and Hidden Markov

optimized through the handover process. Handover (HO)

models. The article de ined Markov chain as a

is a process of passing on an ongoing data session or
mathematical tool to model a transition from one state to

service from one base station within the core network

another based on speci ic probabilistic rules. In addition, into another base station; it is a cross‑layer concept to

the survey highlighted a comparison of the Markov chain support user mobility.

of loading mechanisms and open is‑ sues and challenges

associated with different approaches. A fog‑aided architecture for seamless handover was

proposed by [31]; the proposed architecture includes

a general integration of all types of mobile devices

4. EXISTING TECHNIQUES AND SOLUTIONS and networks and assists Vehicle‑to‑Everything
(V2X) distributed applications by responding to their

The most common infrastructural solutions and tech‑

latency minimization related needs, data privacy and

niques proposed by scholars to achieve seamless service security critical network related requirements. The

provision in mobile cloud computing include fog, edge proposed architecture is leveraged on 5G
computing, handover techniques and femtocell technolo‑ architecture, along with SDN and NFV to achieve

gies. Several algorithms have been proposed to deter‑ proactive, context‑aware, and secure handover
mine the optimal edge computing point, reduce latency of mechanisms. Hence, fog‑enabled architecture and

data traf ic, improve data of loading speed and enhance

SDN‑enabled architecture have been combined in this

security in the MCC environment. Studies on MCC sys‑ approach; the authors assumed that connected

tems highlight that researchers in the ield of mobile vehicles are fog devices with distributed

com‑ puting, software engineering, cloud computing, and

intelligence since vehicles mobility can be predicted

arti‑ icial intelligence have successfully utilized MCC

and they are computing resource rich, they are thus

architec‑ tural models and infrastructures to improve the

equipped with satellite and terrestrial communication
perfor‑ mance of MCC systems through its software.

capabilities. For the SDN enabled architecture part,
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