Formulating Cost-Effective Data Distribution Strategies Online for Edge Cache Systems

Publication Name

IEEE Transactions on Parallel and Distributed Systems


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 Online Edge Data Distribution (OEDD) problem, aiming to minimize app vendors’ total transmission cost, while ensuring low transmission latency in the long term. We first model this problem and prove its $\mathcal {NP}$-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.

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