一般社団法人 電子情報通信学会
THE INSTITUTE OF ELECTRONICS,INFORMATIONCOMMUNICATION ENGINEERS
THE INSTITUTE
OF AND
ELECTRONICS,

信学技報
IEICE Technical Report
SR2018-75IEICE(2018-10)
Technical Report

INFORMATION AND COMMUNICATION ENGINEERS[Poster Presentation] Computational Capability and Radio Resource Allocation
for Mobile Edge Computing
Ryuji KOBAYASHI†

and Koichi ADACHI††Advanced Wireless and Communication Research Center, The University of Electro-Communications,
1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan
E-mail: †{r.kobayashi, adachi}@awcc.uec.ac.jp
Abstract Due to the rapid advancement of the computing power of mobile devices, the interest in mobile applications andservices have been rapidly growing. Since the computing power of mobile devices are not yet to be sufficient to process heavy
tasks, the concept of mobile edge computing (MEC) has attracted huge attention. A MEC server which can process such heavy
tasks is located at the edge of the radio access network (RAN) such as a base station (BS). Thus, mobile user (MU) can request
the MEC server to process the task that it wishes to execute, and get the response within short time. If multiple MUs possesstasks that need to be processed by MEC server, MUs and MEC need to transmit and receive tasks using limited radio resources.
In addition, MEC server also has to handle multiple tasks. Therefore, the radio and computational resources need to beallocated to each MU by taking into account the wireless channel condition and the computational power of MUs and MEC. In
this paper, we propose an allocation scheme of the radio and computation resources to minimize the processing completiontime of tasks which can be divided into local task and offload task. Local task is processed by each MU whilst offload task isprocessed by a MEC server. The processing completion time of the offload tasks are composed of three phases, uploading thetasks, processing the tasks at the MEC server, and returning the computation result from the MEC server to the MU. We first
formulate the optimization problem to minimize the completion time of tasks. For the formulated optimization problem, we
propose a two-step radio and computational resources allocation method which iteratively performs bisection search method
and Johnson's algorithm. The numerical results elucidate that the proposed scheme can reduce the average total completiontime by 33% compared to the conventional schemes.
Keywords Mobile edge computing (MEC), cellular network, resource allocation, optimization
A c k n o w l e d g em e n t
This work was supported by the European
Commission
intheframeworkoftheH2020-EUJ-02-2018 project 5G-Enhance (Grantagreement no.815056) and the Ministry of Internal
Affairs and Communications (MIC) of Japan.

- 37 -This article is a technical report without peer review, and its polished and/or extended version may be published elsewhere.

This article is a technical report without peer review, and its polished and/or extended version may be publishedelsewhere.
Copyright©2018 by IEICE
Copyright ©20by IEICEComputational Capability and Radio Resource Allocation forMobile Edge Computing
Ryuji Kobayashi and Koichi Adachi
Advanced Wireless & Communication Research Center (AWCC), The University of Electro-Communications
Email: {r.kobayashi, adachi}@awcc.uec.ac.jp

Background

Mobile Edge Computing (MEC)• Mobile devices such as smartphone are inevitable in daily life
• A server with high computing power and• Along with high performance of mobile devices, interest in mobilestorage capacity is deployed the edge of theapplications/services (Augmented reality etc.) has been increasing
radio access network (RAN)• Problem in processing such applications: Necessity of high computing power • Server provides computing and storage• The computing power of mobile devices are not yet to be sufficient tocapability to mobile devices
process heavy application tasks
It is possible to process heavy application
Applications are difficult to be processed on mobile devices only
task in short time
Mobile edge computing (MEC) has attracted huge attention

ProblemComputational
Processing: Uplink (UL)
: Downlink (DL)Fig. 1. MECPurpose

Requirement of MEC• Low latency– Due to the increase of applications requiring real-time processing• Effective utilization of limited radio and computation resources– Radio resources (such as frequency and time) necessary for communication– Computational resources (such as CPU) of MEC server for processing

System Model
Multiuser MEC system• One MEC server with a computing power Fmec [CPU cycles/sec] at the BSwith computing power Flocal [CPU cycles/sec] and• U MUs: = 1, 2, … ,dividable tasks• Based on resource allocation result, each task is divided into– Local task: All or part of task that is processed by the local CPU– Offload task: All or part of task that is transmitted to MEC in UL, andwhose computation results from MEC server are received in DL• Scheduling among uploading, computing, and downloading tasks of MUs
are executed in a time-division manner

Proposed Algorithm
Assignment of radio and computational resources by time-division manner• It is essential to determine the local processing time, upload time, processing
time at MEC and return time and scheduling time
Executed by two-step iterative resource allocation method
Time allocationBisection search method
Clock allocation
Johnson’s algorithm
Bisection search determines the time durations of local processing andoffload processing (uploading, processing at MEC, and downloading) for
each MU so that the task completion time of each MU is minimized
Johnson’s algorithm schedules the sequence of offload processing of MUs
offloadlocal
offloadMU 2
local
offload
MU 3
local

Proposing an efficient resource allocation algorithm to minimize theprocessing completion time of application tasks, which consists of• Uploading tasks from mobile users (MUs) to MEC server• Processing the tasks at MEC server• Downloading the computation result from MEC server to MUs

Task model (Parameters of task)> 0 [bits]• Task size
• Application type*> 0 [CPU cycles/bit]• Offload task size
, [bits]• Local task size (, ) [bits]• Computation result size ( , ) [bits]
Interference model• The cell of interest is surrounded byinterfering cells (Fig. 2): MEC time

: DL time

1

2

3, 45

Bisection
SearchJohnson’s
Algorithm

Feasibility
Check

Convergence
Check

Fig. 4. Resource allocation method: LOCAL time(b) Clock allocation[2] H. Q. Le, et al., ”Efficient Resource Allocation in Mobile edge Computation Offloading:Completion Time Minimization”, in Proc. IEEE ISIT, pp. 2513-2517, Aachen, Germany, Jun. 2017.

2

Proposed_Method
ONLY_Bisection
ALL_MEC_Johnson
ALL_MEC
ALL_LOCAL

1

1

About 33%
reduction

Proposed_Method,: UL channel capacity of user u: DL channel capacity of user u

Compared to processing all tasks
locally, offloading tasks to MEC cangreatly reduce computation time
(Fig. 5, 6)

2
The number of MUs is 100,[1] J. Guo, et al., "Energy-Efficient Resource Allocation for Multi-User Mobile Edge Computing", in Proc. IEEE Global Commun. Conf., pp. 1-7, Singapore, Singapore, Dec. 2017.

Simulation & Conclusion
Table1. Simulation parameter [2]Fig. 2. Interference model**The difference between the processing time of the local task and the processing time of offload task becomes lessthan or equal to (very small value)

Fig. 3. Assign task time and clock of each MU

Number of MUs
10, 20, 30, 40, 50, 100in cell of interest
Number of BS
9 (cell of interest: 1)
BS
Coverage range
1000 [m]×1000 [m]Bandwidth
10 [MHz]MU
23 [dBm]
Transmission
powerBS
43 [dBm]
Maximum data rate
6.0 [bps/Hz]
174 [dBm/Hz]
PSD of AWGN
Shadowing deviation
6.0 [dB]
Local CPU{1, 2, …, 10}×108 [CPU cycles/s]
9
MEC CPU
10×10 [CPU cycles/s]{100, 150, …, 300} [kbits]
Data size{5, 6, …, 15}×102 [CPUTask typecycles/bit]
0.0001fComputation result size***,,:Interference MU: Interference cell

Resource allocation method for minimizing total processing time
1. Local processing time, upload time, processing time at the MEC return
time are determined using bisection search method
2. For the time obtained in step1, the order of each task is determined using
the Johnson’s algorithm
3. Processing completion time obtained by step2 (according to the feasibilitycondition) is evaluated
4. The minimum processing completion time is updated
5. Steps 1 to 3 are repeated until convergence**

MU 1: UL time(a) Time allocation:MU of interest: Cell of interest* Describe the number of CPU cycles required to process one bit of task

offloadlocal
offloadMU 2
local
offload
MU 3local

MU 1:BSONLY_Bisection ALL_MEC_Johnson

ALL_MEC

ALL_LOCAL

MethodFig. 5. Comparison of total computation time0204060
Number of MU

80Fig. 6. Impact of the number of MUs

100

When the number of MUs is 100, theproposed method reduces total
computation time by about 33%
compared to existing method (Fig. 6)
As the number of MUs increases,resources can be optimally allocatedcompared to existing method

Acknowledgement
This work was supported by the European Commission in the framework of the H2020-EUJ-02-2018 project 5G-Enhance (Grant agreement no.815056) and the Ministry of Internal Affairs and Communications (MIC) of Japan.

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