Data Caching Optimization With Fairness in Mobile Edge Computing

Article

Zhou, Jingwen, Chen, Feifei, He, Qiang, Xia, Xiaoyu, Wang, Rui and Xiang, Yong. 2023. "Data Caching Optimization With Fairness in Mobile Edge Computing." IEEE Transactions on Services Computing. 16 (3), pp. 1750 - 1762. https://doi.org/10.1109/TSC.2022.3197881
Article Title

Data Caching Optimization With Fairness in Mobile Edge Computing

ERA Journal ID36548
Article CategoryArticle
AuthorsZhou, Jingwen (Author), Chen, Feifei (Author), He, Qiang (Author), Xia, Xiaoyu (Author), Wang, Rui (Author) and Xiang, Yong (Author)
Journal TitleIEEE Transactions on Services Computing
Journal Citation16 (3), pp. 1750 - 1762
Number of Pages12
Year2023
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1939-1374
Digital Object Identifier (DOI)https://doi.org/10.1109/TSC.2022.3197881
Web Address (URL)https://ieeexplore.ieee.org/document/9854206
Abstract

Mobile edge computing (MEC) provides a new computing paradigm that can overcome the inability of the traditional cloud computing paradigm to ensure low service latency by pushing computing power and resources to the network edge. Many studies have attempted to formulate edge data caching strategies for app vendors to optimize caching performance by caching the right data on the right edge servers. However, existing edge data caching approaches have unfortunately ignored fairness, which is an important issue from the app vendor's perspective. In general, an app vendor needs to cache data on edge servers to serve its users with insignificant latency differences at a minimum caching cost. In this paper, we make the first attempt to tackle the fair edge data caching (FEDC) problem. Specifically, we formulate the FEDC problem as a constraint optimization problem (COP) and prove its NP-hardness. An optimal approach named FEDC-OPT is proposed to find optimal solutions to small-scale FEDC problems with integer programming technique. In addition, an approximate algorithm named FEDC-APX is proposed to find approximate solutions in large-scale FEDC problems. The performance of the proposed approaches is analyzed theoretically, and evaluated experimentally on a widely-used real-world data set against four representative approaches. The experimental results show that the proposed approaches can solve the FEDC problem efficiently and effectively.

KeywordsMobile edge computing, fairness, edge data caching, optimization, integer programming, approximation algorithm
ANZSRC Field of Research 2020460612. Service oriented computing
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Byline AffiliationsDeakin University
Swinburne University of Technology
School of Mathematics, Physics and Computing
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Institution of OriginUniversity of Southern Queensland
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