A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

被引:87
作者
Huang, Pei-Qiu [1 ]
Wang, Yong [1 ]
Wang, Kezhi [2 ]
Liu, Zhi-Zhong [1 ]
机构
[1] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China
[2] Northumbria Univ, Dept Comp & Informat Sci, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England
基金
中国国家自然科学基金;
关键词
Task analysis; Servers; Resource management; Optimization; Delays; Mobile handsets; Interference; Ant colony system (ACS); bilevel optimization; computation resource allocation; mobile edge computing (MEC); offloading decision; ANT COLONY OPTIMIZATION; COMPUTATION;
D O I
10.1109/TCYB.2019.2916728
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper studies a multiuser cooperative mobile edge computing offloading (called CoMECO) system in a multiuser interference environment, in which delay-sensitive tasks may be executed on local devices, cooperative devices, or the primary MEC server. In this system, we jointly optimize the offloading decision and computation resource allocation for minimizing the total energy consumption of all mobile users under the delay constraint. If this problem is solved directly, the offloading decision and computation resource allocation are generally generated separately at the same time. Note, however, that they are closely coupled. Therefore, under this condition, their dependency is not well considered, thus leading to poor performance. We transform this problem into a bilevel optimization problem, in which the offloading decision is generated in the upper level, and then the optimal allocation of computation resources is obtained in the lower level based on the given offloading decision. In this way, the dependency between the offloading decision and computation resource allocation can be fully taken into account. Subsequently, a bilevel optimization approach, called BiJOR, is proposed. In BiJOR, candidate modes are first pruned to reduce the number of infeasible offloading decisions. Afterward, the upper-level optimization problem is solved by ant colony system (ACS). Furthermore, a sorting strategy is incorporated into ACS to construct feasible offloading decisions with a higher probability and a local search operator is designed in ACS to accelerate the convergence. For the lower-level optimization problem, it is solved by the monotonic optimization method. In addition, BiJOR is extended to deal with a complex scenario with the channel selection. Extensive experiments are carried out to investigate the performance of BiJOR on two sets of instances with up to 400 mobile users. The experimental results demonstrate the effectiveness of BiJOR and the superiority of the CoMECO system.
引用
收藏
页码:4228 / 4241
页数:14
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