GreenStor: Application-aided energy-efficient storage

The volume of online data content has shown an unprecedented growth in recent years. Fueling this growth are new federal regulations which warrant longer data retention and a general increase in the richness of data content. To cope with this growth, high performance computing and enterprise environments are making use of large disk-based solutions that consume power all the time, unlike tape-based solutions. As a consequence, the energy consumption of the storage solutions has grown significantly. In this work we propose a storage solution called GreenStor, which makes use of application hinting on top of Massive Arrays of Idle Disks (MAID) to improve energy efficiency. GreenStor is centered on MAID, but with more efficient data movement to aid in energy conservation. Specifically, we propose an Extent-based Metadata Manager that achieves better space efficiency without sacrificing cache utilization and an Opportunistic Scheduling scheme that helps provide better use of application hints in a MAID system. Results show that our proposed opportunistic scheme for application hint scheduling consumes up to 40% less energy compared to traditional non-MAID storage solutions, whereas use of standard schemes for scheduling application hints on typical MAID systems is only able to achieve a smaller energy savings of about 25% versus non-MAID storage.