A Scalable Optimization Framework for Storage Backup Operations Using Markov Decision Processes
Explosive growth of data generation and increasing reliance of business analysis on massive data make data loss more damaging than ever before. Thus it has also become a critical issue for businesses to protect important data effectively. In a system with multiple data sets, complex system configurations and data protection requirements, backup planning plays an important role for maintaining the desired level of data protection while minimizing the impact on system operation. In this paper we investigate the use of Markov Decision Process (MDP) to guide the planning of data backup operations. To improve the applicability of the MDP framework to large systems, we present a novel approximation method to enhance its scalability. The benefit of the framework is demonstrated through numerical examples, where our MDP method reduces the storage system downtime by over 50% compared to the best heuristic approach.
