Mobility-Aware and Double Auction-Based Joint Task Offloading and Resource Allocation Algorithm in MEC

In mobile edge computing (MEC), task offloading and resource allocation are two important issues that are inextricably linked. However, existing studies have either ignored the mobility of mobile users (MUs) during task offloading or the allocation of profits between two parties during the allocatio...

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Published inIEEE eTransactions on network and service management Vol. 21; no. 1; pp. 821 - 837
Main Authors Zhang, Lianming, Xiao, Kai, Jin, Lingbo, Dong, Pingping, Tong, Zhao
Format Journal Article
LanguageEnglish
Published New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN1932-4537
1932-4537
DOI10.1109/TNSM.2023.3295406

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Summary:In mobile edge computing (MEC), task offloading and resource allocation are two important issues that are inextricably linked. However, existing studies have either ignored the mobility of mobile users (MUs) during task offloading or the allocation of profits between two parties during the allocation of limited resources (i.e., the resource competition). In this paper, we jointly optimized these two problems. First, to reduce the task offloading delay and the service interruption due to movement, we develop a mobility-aware model, based on which we propose the MWBS algorithm to select the appropriate offloading base station (BS) for MUs. Second, considering the resource competition and the delay constraint of the task, we develop a double auction model and then propose the DARA algorithm, which efficiently allocates the BS resources and maximizes the total system revenue (i.e., social welfare) through a multi-session auction. Finally, we combine MWBS and DARA to propose the BS resource allocation algorithm called MD-BSRA in mobile scenarios. Simulation results show that MD-BSRA can effectively improve task offload success rate, total system revenue and resource utilization while reducing offload delay and service interruption.
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ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2023.3295406