Write off-loading
Dushyanth Narayanan, Austin Donnelly, Antony Rowstron
Abstract
Dushyanth Narayanan, Austin Donnelly, Antony Rowstron
Abstract
In enterprise data centers power usage is a problem impacting server density and the total cost of ownership. Storage uses a significant fraction of the power budget and there are no widely deployed power-saving solutions for enterprise storage systems. The traditional view is that enterprise workloads make spinning disks down ineffective because idle periods are too short. We analyzed block-level traces from 36 volumes in an enterprise data center for one week and concluded that significant idle periods exist, and that they can be further increased by modifying the read/write patterns using write off-loading . Write off-loading allows write requests on spun-down disks to be temporarily redirected to persistent storage elsewhere in the data center. The key challenge is doing this transparently and efficiently at the block level, without sacrificing consistency or failure resilience. We describe our write off-loading design and implementation that achieves these goals. We evaluate it by replaying portions of our traces on a rack-based testbed. Results show that just spinning disks down when idle saves 28--36% of energy, and write off-loading further increases the savings to 45--60%.
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In enterprise data centers power usage is a problem impacting server density and the total cost of ownership. Storage uses a significant fraction of the power budget and there are no widely deployed power-saving solutions for enterprise storage systems. The traditional view is that enterprise workloads make spinning disks down ineffective because idle periods are too short. We analyzed block-level traces from 36 volumes in an enterprise data center for one week and concluded that significant idle periods exist, and that they can be further increased by modifying the read/write patterns using write off-loading . Write off-loading allows write requests on spun-down disks to be temporarily redirected to persistent storage elsewhere in the data center. The key challenge is doing this transparently and efficiently at the block level, without sacrificing consistency or failure resilience. We describe our write off-loading design and implementation that achieves these goals. We evaluate it by replaying portions of our traces on a rack-based testbed. Results show that just spinning disks down when idle saves 28--36% of energy, and write off-loading further increases the savings to 45--60%.
Key concepts: Computer science, Idle, Testbed, Data center, Key (lock), Block (permutation group theory), Resilience (materials science), Power (physics)