4.7 Article

Computation Offloading With Instantaneous Load Billing for Mobile Edge Computing

期刊

IEEE TRANSACTIONS ON SERVICES COMPUTING
卷 15, 期 3, 页码 1473-1485

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TSC.2020.2996764

关键词

Task analysis; Servers; Games; Delays; Edge computing; Cloud computing; Energy consumption; Mobile edge computing; computation offloading; aggregative game; instantaneous load billing

资金

  1. National Key RD Program [2019YFB1803303, 2018YFB1004800]
  2. Natural Science Foundation of Beijing, China [L172049]
  3. Natural Science Foundation of China [61727802, 61872184, 61531006]
  4. US MURI AFOSR MURI [18RT0073]
  5. NSF [EARS-1839818, CNS1717454, CNS-1731424, CNS-1702850]

向作者/读者索取更多资源

Mobile edge computing is a promising approach to reduce task processing latency by offloading tasks from user equipments to servers. This article presents a two-stage computing offloading scheme to minimize task processing delay and manage server load properly.
Mobile edge computing (MEC) is a promising approach that can reduce the latency of task processing by offloading tasks from user equipments (UEs) to MEC servers. Existing works always assume that the MEC server is capable of executing the offloaded tasks, without considering the impact of improper load on task processing efficiency. In this article, we present a two-stage computing offloading scheme to minimize the task processing delay while managing the server load properly. To minimize the task processing delay, each UE optimizes how much workload to be offloaded to the MEC server. To improve the task processing efficiency of the server, we arrange the processing order of offloading tasks by introducing an aggregative game with an instantaneous load billing mechanism. The proposed game can obtain the optimal task offloading and processing strategy with limited information and a small number of iterations. Simulation results show that our scheme approaches the optimal offloading strategy in terms of minimizing task processing delay for each UE and improving processing efficiency for the server.

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