Joint Service Caching and Computing Offloading Strategies for Electrical Equipment Intelligent IoT Platform

doi:10.11930/j.issn.1004-9649.202110035

Abstract

Abstract: Mass data constitutes a challenge to the electrical equipment intelligent industrial Internet of Things (EIP), and it is urgently needed for edge computing to assist the real-time processing of data. Current works mainly focus on the application of edge computing in Industrial Internet of Things(IIoT), while the impact of service caching strategy on edge computing is not paid enough attention. Aiming to solve above problem, a joint service caching and task offloading strategy for EIP was proposed. In order to solve the revenue conflict between service providers and equipment suppliers, we investigated and proved the existence of a two-stage Stackelberg game and the Nash equilibrium(NE). Further more, we proposed a generalized Benders decomposition algorithm combined with particle swarm optimization to solve the mixed integer programming efficiently. The simulation results show that the proposed strategy can effectively improve the task processing efficiency, reduce the computing cost of suppliers, and improve the service revenue of the service providers.

Key words: electrical equipment, edge computing, task offloading, service caching, cost optimization, industrial Internet

SUN Yi, CHANG Shaonan, CHEN Kai, CUI Qiang, SHEN Weijie. Joint Service Caching and Computing Offloading Strategies for Electrical Equipment Intelligent IoT Platform[J]. Electric Power, 2022, 55(4): 23-32,43.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202110035

https://www.electricpower.com.cn/EN/Y2022/V55/I4/23

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