Energy–latency tradeoffs edge server selection and DQN-based resource allocation schemes in MEC

Chunlin Li, Zewu Ke, Qiang Liu,Cong Hu, Chengwei Lu,Youlong Luo

WIRELESS NETWORKS(2023)

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Abstract
This paper discusses the challenges of mobile edge computing with low latency and energy consumption caused by the explosive growth of communication traffic and data generated by mobile devices. To address the issue of selecting edge servers, a multi-user-oriented edge server selection strategy is proposed. The strategy minimizes the weighted sum of network latency and total energy consumption and develops an improved SPEA2-based algorithm to choose the most suitable edge server. The resource allocation problem is also considered, and a resource allocation strategy is proposed that maximizes the energy efficiency ratio. A DQN-based resource allocation strategy is devised to find the best resource allocation strategy. Experimental results demonstrate that the proposed server selection strategy reduces system overhead in terms of energy consumption and latency while improving resource utilization. The resource allocation strategy improves the computational efficiency of edge servers while reducing latency and total energy consumption.
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Key words
Mobile edge computing,Edge server selection,Resource allocation,Deep Q-learning
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