Data Caching Optimization in the Edge Computing Environment

Article


Liu, Ying, He, Qiang, Zheng, Dequan, Xia, Xiaoyu, Chen, Feifei and Zhang, Bin. 2022. "Data Caching Optimization in the Edge Computing Environment." IEEE Transactions on Services Computing. 15 (4), pp. 2074-2085. https://doi.org/10.1109/TSC.2020.3032724
Article Title

Data Caching Optimization in the Edge Computing Environment

ERA Journal ID36548
Article CategoryArticle
AuthorsLiu, Ying (Author), He, Qiang (Author), Zheng, Dequan (Author), Xia, Xiaoyu (Author), Chen, Feifei (Author) and Zhang, Bin (Author)
Journal TitleIEEE Transactions on Services Computing
Journal Citation15 (4), pp. 2074-2085
Number of Pages12
Year2022
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1939-1374
Digital Object Identifier (DOI)https://doi.org/10.1109/TSC.2020.3032724
Web Address (URL)https://ieeexplore.ieee.org/document/9234716
Abstract

With the rapid increase in the use of mobile devices in people’s daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed in close proximity to mobile users, caching popular data on edge servers can ensure mobile users’ low-latency access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data caching problem with a focus on the reduction of network delay and the improvement of mobile devices’ energy efficiency. In this article, we tackle this data caching problem in the edge computing environment from a service provider’s perspective with the aim to maximize its data caching revenue. This problem is challenging because there is a trade-off between the benefit produced and the cost incurred by caching data on edge servers. In the meantime, the constraint for data access latency must also be fulfilled. In this article, we formulate the data caching problem in the edge computing environment as an integer programming (IP) problem and prove its NP-completeness. To solve this problem effectively and efficiently in large-scale scenarios, we propose an approximation approach to find near-optimal solutions. Extensive experiments are conducted on a widely-used real-world dataset to evaluate our approaches.

Keywordsdata caching; data popularity; Edge computing; Integer Problem (IP); near-optimal algorithm; optimization
ANZSRC Field of Research 2020460612. Service oriented computing
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Byline AffiliationsNortheastern University, China
Swinburne University of Technology
Deakin University
Institution of OriginUniversity of Southern Queensland
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