Data Caching Optimization With Fairness in Mobile Edge Computing
Article
Article Title | Data Caching Optimization With Fairness in Mobile Edge Computing |
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ERA Journal ID | 36548 |
Article Category | Article |
Authors | Zhou, Jingwen (Author), Chen, Feifei (Author), He, Qiang (Author), Xia, Xiaoyu (Author), Wang, Rui (Author) and Xiang, Yong (Author) |
Journal Title | IEEE Transactions on Services Computing |
Journal Citation | 16 (3), pp. 1750 - 1762 |
Number of Pages | 12 |
Year | 2023 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 1939-1374 |
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. |
Keywords | Mobile edge computing, fairness, edge data caching, optimization, integer programming, approximation algorithm |
ANZSRC Field of Research 2020 | 460612. Service oriented computing |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Deakin University |
Swinburne University of Technology | |
School of Mathematics, Physics and Computing | |
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7q46/data-caching-optimization-with-fairness-in-mobile-edge-computing
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