Proactive and Adaptive Data Migration in Hierarchical Storage Systems using Reinforcement Learning Agent

With the data generation rates growing exponentially, businesses are having a difficult time maintaining data center infrastructure. Hierarchical storage systems has evolved as a better alternate to managing data, as frequently accessed data is placed on higher tiers and the least frequently accessed data on lower tiers. But the data arrangement is not always static. Data Migration is an operation in which the selected data is physically moved, or migrated, to different storage components. The existing method of data migration in hierarchical storage systems has several shortcomings – reactive, heuristic policies and proprietary software. This paper attempts to overcome the above limitations by applying a reinforcement learning (RL) agent in the data migration of hierarchical storage systems. By the addition of RL agent the data migration which earlier was responsive, is made proactive and adaptive. Experimental results demonstrate the effectiveness of the proposed RL agent. The results indicate that: (i) the average queue size of storage devices is reduced to almost zero as the RL agent proactively migrates data and (ii) at 95 % confidence level the RL agent has no affect on the read and write operation of certain file sizes