Proactive Data Migration for Improved Storage Availability in Large-Scale Data Centers

In face of high partial and complete disk failure rates and untimely system crashes, the executions of low-priority background tasks become increasingly frequent in large-scale data centers. However, the existing algorithms are all reactive optimizations and only exploit the temporal locality of workloads to reduce the user I/O requests during the low-priority background tasks. To address the problem, this paper proposes Intelligent Data Outsourcing (IDO), a zone-based and proactive data migration optimization, to significantly improve the efficiency of the low-priority background tasks. The main idea of IDO is to proactively identify the hot data zones of RAID-structured storage systems in the normal operational state. By leveraging the prediction tools to identify the upcoming events, IDO proactively migrates the data blocks belonging to the hot data zones on the degraded device to a surrogate RAID set in the large-scale data centers. Upon a disk failure or crash reboot, most user I/O requests addressed to the degraded RAID set can be serviced directly by the surrogate RAID set rather than the much slower degraded RAID set. Consequently, the performance of the background tasks and user I/O performance during the background tasks are improved simultaneously. Our lightweight prototype implementation of IDO and extensive trace-driven experiments on two case studies demonstrate that, compared with the existing state-of-the-art approaches, IDO effectively improves the performance of the low-priority background tasks. Moreover, IDO is portable and can be easily incorporated into any existing algorithms for RAID-structured storage systems.