Formulating cost-effective data distribution strategies online for edge cache systems

Xiaoyu Xia, Feifei Chen, Qiang He, John Grundy, Mohamed Abdelrazek, Athman Bouguettaya, Jun Shen, Hai Jin

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)

Abstract

Edge Computing (EC) enables a new kind of caching system in close geographic proximity to end-users by allowing app vendors to cache popular data on edge servers deployed at base stations. This edge cache system can better support latency-sensitive applications. However, transmitting data from the centralized cloud to the edge servers without proper transmission strategies may cost app vendors dearly. Cost-effective data distribution strategies are of particular importance for applications, whose data to be cached at the edge often changes dynamically. In this paper, we study this <italic>Online Edge Data Distribution</italic> (OEDD) problem, aiming to minimize app vendors&#x2019; total transmission cost, while ensuring low transmission latency in the long term. We first model this problem and prove its <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula>-hardness. We then combine Lyapunov optimization and game theory to propose a novel Latency-Aware Online (LAO) approach for solving this OEDD problem over time in a distributed manner with provable performance guarantees. The evaluation of LAO based on a real-world dataset demonstrates that it can help app vendors formulate cost-effective edge data distribution strategies in an online manner.

Original languageEnglish
Pages (from-to)4270-4281
Number of pages12
JournalIEEE Transactions on Parallel and Distributed Systems
Volume33
Issue number12
DOIs
Publication statusPublished - 1 Dec 2022

Keywords

  • Australia
  • Cloud computing
  • Costs
  • Data communication
  • data distribution
  • edge cache system
  • online algorithm
  • optimization
  • Optimization
  • Servers
  • Videos

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