期刊
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
卷 25, 期 4, 页码 2389-2401出版社
SPRINGER
DOI: 10.1007/s10586-021-03414-0
关键词
Edge computing; Offloading computation; Cache-enabled; Cache replacement
资金
- International Science and Technology Cooperation Projects of Guangdong Province [2020A0505100060]
- Natural Science Foundation of Guangdong Province [2021A1515011392/2021A1515011812]
- Guangzhou University [YK2020008/YJ2021003]
This paper investigates a multi-user cache-enabled mobile edge computing network and proposes an intelligent particle swarm optimization (PSO) based offloading strategy with cache mechanism. The proposed cache replacement strategy can effectively reduce the system latency and energy consumption for the future networks.
With the development of Internet of Things (IoT) devices and the growth of users' demand for computation and real-time services, artificial intelligence has been applied to reduce the system cost for future network systems. To meet the demand of network services, the paradigm of edge networks is increasingly shifting towards the joint design of computation, communication and caching services. This paper investigates a multi-user cache-enabled mobile edge computing (MEC) network and proposes an intelligent particle swarm optimization (PSO) based offloading strategy with cache mechanism. In each time slot, the server selects one file among multiple ones to pre-store, according to the proposed cache replacement strategy. In the next time slot, the requested files by the users needn't to be computed and offloaded, if these files have been cached in the server. For the files that have not been cached in the server, PSO algorithm is adopted to find an appropriate offloading ratio to implement the partial offloading. Simulation results are finally presented to validate the proposed studies. In particular, we can find that incorporating the proposed cache replacement strategy into the computation offloading can effectively reduce the system latency and energy consumption for the future networks.
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