Popularity-Aware Data Placement in Erasure Coding-Based Edge Storage Systems

Abstract

Edge computing enables low-latency data access by caching popular content on edge servers. However, server unavailability at runtime can increase retrieval latency when requests are redirected to the cloud. To enhance availability, erasure coding (EC) has been employed to ensure full data access for all users in an edge storage system (ESS). Existing approaches for edge data placement place coded blocks across the entire system without considering data popularity. As a result, they often suffer from high data retrieval latency. In addition, they are designed to process data items individually. Data placed earlier will limit the placement options for subsequent files because edge servers with the most neighbors in the system can be easily exhausted. Some files cannot be placed properly to accommodate user demands. This increases users’ data retrieval latency further. This paper investigates the edge data placement (EDP) problem with popularity awareness. We formulate EDP as a mixed-integer programming problem and prove its $\mathcal{NP}$-hardness. We then design an exact algorithm (EDP-O) that decomposes the problem into three convex subproblems and solves it iteratively, and an approximation algorithm (EDP-A) with a guaranteed $\ln N$ approximation ratio for large-scale systems. Experiments on real-world datasets show that EDP-O and EDP-A reduce average retrieval latency by 18.4% and 15.6% in small-scale settings, while EDP-A achieves 54.7% latency reduction and 34.9% lower discard rate in large-scale scenarios compared to four baselines.