Formulating Interference-aware Data Delivery Strategies in Edge Storage Systems
Date
2022
Authors
Xia, X.
Chen, F.
He, Q.
Cui, G.
Grundy, J.
Abdelrazek, M.
Dong, F.
Editors
Advisors
Journal Title
Journal ISSN
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Conference paper
Citation
Proceedings of the International Conference on Parallel Processing, 2022, pp.68-1-68-11
Statement of Responsibility
Xiaoyu Xia, Feifei Chen, Qiang He, Guangming Cui, John Grundy, Mohamed Abdelrazek, Fang Dong
Conference Name
International Conference on Parallel Processing (ICPP) (29 Aug 2022 - 1 Sep 2022 : Virtual, Online)
Abstract
Networked edge servers constitute an edge storage system in edge computing (EC). Upon users’ requests, data must be delivered from edge servers in the system or from the cloud to users. Existing studies of edge storage systems have unfortunately neglected the fact that an excessive number of users accessing the same edge server for data may impact users’ data rates seriously due to the wireless interference. Thus, users must first be allocated to edge servers properly for ensuring their data rates. After that, requested data can be delivered to users to minimize their average data delivery latency. In this paper, we formulate this Interference-aware Data Delivery at the network Edge (IDDE) problem, and demonstrate its NP-hardness. To tackle it effectively and efficiently, we propose IDDE-G, a novel approach that first finds a Nash equilibrium as the strategy for allocating users. Then, it finds an approximate strategy for delivering requested data to allocated users. We analyze the performance of IDDE-G theoretically and evaluate its performance experimentally to demonstrate the effectiveness and efficiency of IDDE-G on solving the IDDE problem.
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