4.7 Article

Multi-Objective Parallel Task Offloading and Content Caching in D2D-Aided MEC Networks

期刊

IEEE TRANSACTIONS ON MOBILE COMPUTING
卷 22, 期 11, 页码 6599-6615

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TMC.2022.3199876

关键词

Mobile edge computing; D2D communication; parallel task offloading; content caching; multi-objective optimization

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This paper investigates the optimization problem of content caching and parallel task offloading in device-to-device aided mobile edge computing networks. An enhanced binary particle swarm optimization algorithm and an improved multi-objective bat algorithm are proposed to solve the problem. Experimental results show that the algorithms can significantly reduce delay and energy consumption, and maintain a high parallel task offloading ratio even with a large number of mobile devices.
In device to device (D2D) aided mobile edge computing (MEC) networks, by implementing content caching and D2D links, the edge server and nearby mobile devices can provide task offloading platforms. For parallel tasks, proper decisions on content caching and task offloading help reduce delay and energy consumption. However, what is often ignored in the previous works is the joint optimization of parallel task offloading and content caching. In this paper, we aim to find optimal content caching and parallel task offloading strategies, so as to minimize task delay and energy consumption. The minimization problem is formulated as a multi-objective optimization problem, concerning both content caching and parallel task offloading. The content caching is formulated as an integer knapsack problem (IKP). To solve the IKP problem, an enhanced Binary Particle Swarm Optimization algorithm is proposed. The parallel task offloading problem is formulated as a constrained multi-objective optimization problem, an improved multi-objective bat algorithm is proposed to address the problem. Experimental results show that our algorithm can decrease delay and energy cost by at most 45% and 56%, respectively. In addition, the parallel task offloading ratio remains over 91% even with large number of mobile devices (MDs).

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