AIR: an approximate intelligent redistribution approach to accelerate RAID scaling

Abstract

Nowadays, videos and images are becoming the predominant format of data storage, which take up more space than conventional plain texts. This rapid increment brings a high requirement on scalability in large data centers, where disk arrays (also referred to as "RAID") are the main devices to store the numerous data. To improve the scalability of RAID systems, several scaling approaches are proposed to guarantee a uniform data distribution, such as decreasing migration I/Os and speeding up the scaling process. However, typical approaches are offline and ignore the impacts of concurrent application I/Os, which plays an important role on RAID scaling (i.e., data migration, data distribution, etc.). For example, an exact uniform data distribution among disks doesn't mean an even I/O accesses to these disks. To address this problem, in this paper, we propose an approximate intelligent redistribution (AIR) approach to accelerate RAID scaling. The main idea of AIR is utilizing the dynamic data access patterns from concurrent application workloads, and providing an approximate data distribution to guarantee a uniform I/O accesses to various data disks. To achieve this goal, AIR utilizes the prevailing machine learning algorithms to identify hot data from application workloads, and gives an intelligent migration approach to minimize the data movements. By this way, AIR can sharply cut down the migration I/Os. To demonstrate the effectiveness of AIR approach, we conduct several simulations via Disksim. The results show that, compared to traditional RAID scaling approaches such as FastScale and GSR, AIR saves up to 99.3% I/O cost and reduces the data migration ratio by up to 95.8%, which speeds up the scaling process by a factor of up to 30.3X.