RECaching: Cost-Effective Edge Caching for Cloud Storage With Differentiated Regional Workloads

For cloud storage, edge caching not only reduces the latency of delivering data to requesters due to closer delivery distances, but also potentially brings cost-savings to cloud users due to cheaper edge resources. However, cost-effective caching requires the exact knowledge of future requests, which is hard for cloud users to obtain in advance. It raises a risk of incurring more costs due to unpopular redundant replicas if data is blindly cached at the edge. To overcome this challenge, in this paper, we propose an online cost-effective caching algorithm based on reinforcement learning to dynamically make decisions of edge caching and caching lifespan online without any knowledge of the future. Further, we propose several mechanisms to enhance the cost performance of the proposed algorithm, to make up for possible mistakes made at the beginning of the learning and avoid cost-ineffective periods in caching lifespans. We then analyze the performance of caching decisions given by the proposed algorithm with enhancements. Finally, we conduct extensive simulations driven by real-world traces under prevalent pricing schemes of both the cloud and the edge, which reveals that significant cost-savings and superiority over benchmark algorithms can be achieved.