Graph-based data caching optimization for edge computing

Xiaoyu Xia, Feifei Chen, Qiang He, Guangming Cui, Phu Lai, Mohamed Abdelrazek, John Grundy, Hai Jin

Research output: Contribution to journalArticleResearchpeer-review


Edge computing has emerged as a new computing paradigm that allows computation and storage resources in the cloud to be distributed to edge servers. Those edge servers are deployed at base stations to provide nearby users with high-quality services. Thus, data caching is extremely important in ensuring low latency for service delivery in the edge computing environment. To minimize the data caching cost and maximize the reduction in service latency, we formulate this Edge Data Caching (EDC) problem as a constrained optimization problem in this paper. We prove the NP-completeness of this EDC problem and provide an optimal solution named IPEDC to solve this problem based on Integer Programming. Then, we propose an approximation algorithm named AEDC to find approximate solutions with a limited bound. We conduct intensive experiments on a real-world data set and a synthesized data set to evaluate our approaches. Our results demonstrate that IPEDC and AEDC significantly outperform the four representative baseline approaches.

Original languageEnglish
Pages (from-to)228-239
Number of pages12
JournalFuture Generation Computer Systems
Publication statusPublished - Dec 2020


  • Edge computing
  • Edge data caching
  • Optimization

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