Research Article
Privacy-Aware Task Allocation with Service Differentiation for Mobile Edge Computing: Multi-armed Bandits Approach
@INPROCEEDINGS{10.1007/978-3-031-34790-0_7, author={Hangfan Li and Lin Shi and Xiaoxiong Zhong and Yun Ji and Sheng Zhang}, title={Privacy-Aware Task Allocation with Service Differentiation for Mobile Edge Computing: Multi-armed Bandits Approach}, proceedings={Communications and Networking. 17th EAI International Conference, Chinacom 2022, Virtual Event, November 19-20, 2022, Proceedings}, proceedings_a={CHINACOM}, year={2023}, month={6}, keywords={Mobile Edge Computing (MEC) Privacy Preserving Multi-armed Bandits (MAB)}, doi={10.1007/978-3-031-34790-0_7} }- Hangfan Li
Lin Shi
Xiaoxiong Zhong
Yun Ji
Sheng Zhang
Year: 2023
Privacy-Aware Task Allocation with Service Differentiation for Mobile Edge Computing: Multi-armed Bandits Approach
CHINACOM
Springer
DOI: 10.1007/978-3-031-34790-0_7
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,PrivacyUpperConfidenceBound (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.
