Abstract
With the development of fifth generation (5G) technology, mobile edge computing (MEC) is becoming an essential architecture which is envisioned as a cloud extension version. MEC system can push the resources from cloud side to edge side, aiming to solve many computation intensive problems. The task offloading policy is vital and has an important influence on MEC system. Meanwhile, privacy leakage may occur during the task offloading period which may degrade MEC system performance. The attention on these issues is lack according to existing works. Inspired by this, we present a privacy-preserving aware Multi-Armed Bandits based task allocation algorithm, Privacy Upper Confidence Bound (pUCB), to find a balance between the privacy preserving and the efficiency of task processing. In addition, we take regret analysis of the proposed algorithm. The extensive simulation results show that pUCB scheme can achieve a higher optimal rate, a lower lock rate and less total time cost comparing with traditional Multi-arm bandits (MAB) based algorithm.
H. Li and L. Shi—Co-first authors. This work was supported by The Major Key Project of PCL (Grant No. PCL2021A02), National Natural Science Foundation of China (Grant Nos. 61802221) and the Guangdong Talent Project 2021TQ06X117.
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, H., Shi, L., Zhong, X., Ji, Y., Zhang, S. (2023). Privacy-Aware Task Allocation with Service Differentiation for Mobile Edge Computing: Multi-armed Bandits Approach. In: Gao, F., Wu, J., Li, Y., Gao, H. (eds) Communications and Networking. ChinaCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 500. Springer, Cham. https://doi.org/10.1007/978-3-031-34790-0_7
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DOI: https://doi.org/10.1007/978-3-031-34790-0_7
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