Joint Optimization Strategy of Computation Offloading and Resource Allocation in Multi-Access Edge Computing Environment

In order to help user terminal devices (UTDs) efficiently handle computation-intensive and time-delay sensitive computing task, multi-access edge computing (MEC) has been proposed. However, due to the differences among the performance of UTDs, and the resource limitation of MEC servers, the joint op...

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Vydáno v:IEEE transactions on vehicular technology Ročník 69; číslo 9; s. 10214 - 10226
Hlavní autoři: Li, Huilin, Xu, Haitao, Zhou, Chengcheng, Lu, Xing, Han, Zhu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Computation offloading
Delays
Edge computing
Energy conservation
Energy consumption
genetic algorithm
Genetic algorithms
MEC
MINP
Mixed integer
Mobile computing
Nonlinear programming
Optimization
Power consumption
Resource allocation
Resource management
Servers
Task analysis
ISSN:0018-9545, 1939-9359
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Shrnutí:In order to help user terminal devices (UTDs) efficiently handle computation-intensive and time-delay sensitive computing task, multi-access edge computing (MEC) has been proposed. However, due to the differences among the performance of UTDs, and the resource limitation of MEC servers, the joint optimization between the offloading decisions of UTDs and the allocation of resources in network is still a focus of the research. This paper studies the joint computation offloading and resource allocation strategy in multi-user and multi-server scenarios. Firstly, we formulate the joint optimization problem of computation offloading and resource allocation as a mixed integer nonlinear programming (MINP) problem to minimize the energy consumption of UTDs, by constraining the offloading decision, channel selection, power allocation and resource allocation. Secondly, we propose a two-stage heuristic optimization algorithm based on genetic algorithms, which divides the joint optimization problem of computation offloading and resource allocation in two stages. Based on the coupling relationship between the offloading decision and the resource allocation scheme, we iteratively update the solution of the problem, and finally obtain the stable convergence solution of the optimization problem. Finally, the proposed algorithm is compared with other classical methods to prove the effectiveness.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.3003898