Code Caching-Assisted Computation Offloading and Resource Allocation for Multi-User Mobile Edge Computing

被引:33
作者
Chen, Zhixiong [1 ]
Zhou, Zhaokun [2 ]
Chen, Chen [1 ]
机构
[1] Chongqing Univ, Sch Microelect & Commun Engn, Chongqing 400044, Peoples R China
[2] Chongqing Univ, Chongqing Automot Collaborat Innovat Ctr, Chongqing 400044, Peoples R China
来源
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT | 2021年 / 18卷 / 04期
关键词
Task analysis; Resource management; Delays; Energy consumption; Servers; Cloud computing; Optimization; Caching; computation offloading; mobile edge computing; resource allocation; MINIMIZATION; MANAGEMENT; PLACEMENT; CLOUDS; POLICY;
D O I
10.1109/TNSM.2021.3103533
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Utilizing the data caching technology to reduce data transmission is a promising technique for improving the performance of mobile edge computing (MEC), because the delay and energy consumption produced by data transmission constitute the dominant cost of task execution in MEC. Besides, computation tasks generally consist of input parameters, executive codes, and computation results. The executive codes are fixed and can output difference computation results under different input parameters. Motivated by this, we consider to proactively cache executive codes of tasks at the MEC server to reduce the weighted sum of task execution delay and users' energy consumption. Aiming at establishing optimal system design, we formulate the problem as a non-linear programming problem which involves jointly optimizing the executive code caching strategy, computation offloading decision, wireless resource allocation, and computing resource allocation. We propose to find the optimal solution by employing an alternating optimization framework. The optimal wireless resource and computing resource allocation problem are firstly addressed by utilizing convex optimization technology. Then, a dynamic programming-based algorithm has been developed to achieve the optimal executive code caching and computation offloading strategies. Extensive simulation results show that the proposed scheme operates well and can substantially reduce the system cost over other benchmark schemes.
引用
收藏
页码:4517 / 4530
页数:14
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