Journal:IEEE ICWS2021
Abstract:Edge data caching has attracted tremendous at- tention in recent years. Service providers can cache data on edge servers to serve their users with low data retrieval latency. The key to edge data caching is caching data on the right edge servers to achieve the optimization objective, e.g., minimizing data retrieval latency, minimizing data caching cost, etc. However, Quality of Experience (QoE), which impacts service providers’ caching benefit significantly, has not been adequately considered in existing studies of edge data caching. This is not a trivial issue because QoE and Quality-of-Service (QoS) are not correlated linearly. It significantly complicates the formulation of cost-effective edge data caching strategies under the caching budget, limiting the number of cache spaces to hire on edge servers. In this paper, we study this problem of QoE-aware edge data caching, intending to optimize users’ overall QoE under the caching budget. We first build the opti- mization model and prove the NP-completeness of this problem. To solve this problem efficiently in large-scale scenarios, we propose a heuristic approach and prove its approximation ratio theoretically. Extensive experiments are conducted to compare the performance of the proposed approach against state-of-the- art approaches.
Key Words:QoE aware data caching, edge computing, multiple knapsack problem, budget constraint, approximate algorithm
Indexed by:EI检索
Translation or Not:no