Online collaborative data caching in edge computing

Xiaoyu Xia, Feifei Chen, Qiang He, John Grundy, Mohamed Abdelrazek, Hai Jin

Research output: Contribution to journalArticleResearchpeer-review

88 Citations (Scopus)


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 NPNP-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.

Original languageEnglish
Pages (from-to)281-294
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number2
Publication statusPublished - 1 Feb 2021


  • data caching
  • Edge computing
  • online algorithm

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