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Early Lessons from Energy-Saving Storage Systems Research

Early Lessons from Energy-Saving Storage Systems Research. Sara Alspaugh and Arka Bhattacharya. Exploring the Design Space. Saving Energy At the Block Device Level. Redundancy: reconstruct request from parity group on active disks and cache (Examples: RIMAC, Diverted Access, PARAID, EERAID).

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Early Lessons from Energy-Saving Storage Systems Research

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  1. Early Lessons from Energy-Saving Storage Systems Research Sara Alspaugh and Arka Bhattacharya Exploring the Design Space Saving Energy At the Block Device Level Redundancy: reconstruct request from parity group on active disks and cache (Examples: RIMAC, Diverted Access, PARAID, EERAID) Caching and Layout: keep less frequently accessed data on spun down disks, eject blocks to active disks (Examples: MAID, PA-LRU, PB-LRU, Hibernator, EED, PDC) Choosing the Best Scheme Multispeed Disks: adjust disk speed according to workload demands and performance requirements (Examples: Hibernator, DPRM) SSD Opportunities and Challenges Increased skew results in decreased latency, but write-dominated workloads have lower latencies than their read-dominated counterparts – an artifact of DiskSim. When caching-based techniques are used especially with SSDs, power and performance are correlated. Comparison of 95th percentile latencies for varying cache replacement algorithms and number of SSDs. Takeaways Most promising: hybrid of SSDs and traditional disks, place most frequently accessed data on SSDs (does not preclude use of redundancy-based techniques) Cache- and layout-based strategies that reduce power consumption also improve performance; redundancy-based strategies do not have this effect Assumptions about the workload play an important role SSDs not a panacea: cost, black box flash translation layer Effects of Workload Characteristics Left: Idle periods longer than 10 seconds represent opportunities to spin down disks to save power. Right: Most workloads display a high degree of skew. In this MSR build server trace, 75% of the requests go to 35% of the blocks. Future Work Optimizations: prefetching, zero-copy Using file system and above level knowledge (super challenge: co-located storage and computation) Considerations of cost and capacity requirements

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