A Reinforcement Learning Based System for Minimizing Cloud Storage Service Cost

Currently, many web applications are deployed on cloud storage service provided by cloud service providers (CSPs). A CSP offers different types of storage including hot, cold and archive storage and sets unit prices for these different types, which vary substantially. By properly assigning the data files of a web application to different types of storage based on their usage profiles and the CSP’s pricing policy, a cloud customer potentially can achieve substantial cost savings and minimize the payment to the CSP. However, no previous research handles this problem. Towards this goal, we present a Markov Decision Process formulation for the cost minimization problem, and then develop a reinforcement learning based approach to effectively solve the problem, which changes the type of storage of each data file periodically to minimize money cost in long term. We then propose a method to aggregate concurrently requested data files to further reduce the cloud storage service payment for a web application. Our experiments with Wikipedia traces show the effectiveness of the proposed methods for minimizing cloud customer cost in comparison with other methods.