Online Collaborative Data Caching in Edge Computing

• Published in: IEEE transactions on parallel and distributed systems Vol. 32; no. 2; pp. 281 - 294
• Main Authors: Xia, Xiaoyu, Chen, Feifei, He, Qiang, Grundy, John, Abdelrazek, Mohamed, Jin, Hai
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Caching; Cloud computing; Collaboration; data caching; Data models; Distributed databases; Edge computing; Mobile handsets; online algorithm; Optimization; Servers
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2020.3016344

In the edge computing (EC) environment, edge servers are deployed at base stations to offer highly accessible computing and storage resources to nearby app users. From the app vendor's perspective, caching data on edge servers can ensure low latency in app users' retrieval of app data. However, an edge server normally owns limited resources due to its limited size. In this article, we investigate the collaborative caching problem in the EC environment with the aim to minimize the system cost including data caching cost, data migration cost, and quality-of-service (QoS) penalty. We model this collaborative edge data caching problem (CEDC) as a constrained optimization problem and prove that it is NP-complete. We propose an online algorithm, called CEDC-O, to solve this CEDC problem during all time slots. CEDC-O is developed based on Lyapunov optimization, works online without requiring future information, and achieves provable close-to-optimal performance. CEDC-O is evaluated on a real-world data set, and the results demonstrate that it significantly outperforms four representative approaches.