Joint Task Offloading and Resource Allocation for Cooperative Mobile-Edge Computing Under Sequential Task Dependency

被引:20
|
作者
Li, Xiang [1 ]
Fan, Rongfei [2 ]
Hu, Han [1 ]
Zhang, Ning [3 ]
机构
[1] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Sch Cyberspace Sci & Technol, Beijing 100081, Peoples R China
[3] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada
基金
中国国家自然科学基金;
关键词
Cooperative mobile-edge computing (MEC); joint allocation of communication and computation resources; sequential task; task offloading; COMMUNICATION; CONVERGENCE; OPTIMIZATION; INTELLIGENT; COMPUTATION; NETWORKS; 6G;
D O I
10.1109/JIOT.2022.3188933
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The emergence of mobile-edge computing (MEC) makes it possible to run intelligent applications on Internet of Things (IoT) devices. However, due to blockage or deep fading, one IoT device may not have direct link with the edge server. In this case, many surrounding wireless devices can serve as a cooperative node. In this article, we study a cooperative MEC system running sequential task, which is composed of a series of subtasks and can support many intelligent applications. To minimize the energy consumption of the IoT device and cooperative node, a task offloading policy together with the allocation of communication and computation resources is designed jointly. The cases when the cooperative node has no/has private task to complete are investigated, which are denoted as cases I and II, respectively. Although both cases involve the optimization of integer variables, their optimal solutions are achieved. For the first case, the associated problem is simplified equivalently and then decomposed into two levels, with the upper level dealing with integer variables and the lower level handling continuous variables. Bisection search is employed to reach optimality in the lower level and the searching space is compressed in the upper level. For the second case, the associated problem is subdivided into three subproblems. To solve every subproblem optimally, a similar operation like case I is followed, with a semiclosed form solution derived in the lower level. Numerical results verify the effectiveness of our proposed methods compared with benchmark methods and our effort on reducing computation complexity.
引用
收藏
页码:24009 / 24029
页数:21
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