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Coming to Grips with the Power Proportional (Data) Storage Problem

Coming to Grips with the Power Proportional (Data) Storage Problem . Sara Alspaugh and Arka Bhattacharya. State of The Art : case 1 solved, case 2 solved in many instances - yet unclear if done in practice case 3 open. What : power proportional storage storage paradigms:

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Coming to Grips with the Power Proportional (Data) Storage Problem

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  1. Coming to Grips with the Power Proportional (Data) Storage Problem Sara Alspaugh and Arka Bhattacharya

  2. State of The Art: • case 1 solved, case 2 solved in many instances • - yet unclear if done in practice • case 3 open What: • power proportional storage • storage paradigms: case 1: static, replicated case 2: monolithic, dedicated case 3: co-located with computation Results: New Ideas: • per-rack SSD cache (case 3) • power-proportional structured storage contributions (case 2) more good less good

  3. Storage Architectures

  4. 1 . Web Farms • Examples: most websites • State of the art: easily made power proportional (Chen [SIGMETRICS ‘05], NapSac [SIGCOMM GreenNets ‘09], etc.) • mostly static, replicated content , serving identical requests

  5. 2 . Monolithic Storage Tier • Examples: search (in-memory indexes), email (disk), etc. – CFS, SAN, transaction tier • State of the art: power proportional distributed file systems (Sierra [MSR-TR ‘09], Rabbit [SOCC ‘10]) and power proportional SAN/RAID arrays (Hibernator [SOSP ‘05], etc.) • Opportunities in structured storage • Trade-off load balancing, replication, fault-tolerance, read performance versus consistency, write performance, power • exactly how depends on storage model and level of abstraction

  6. 3 . Distributed Storage and Computation Co-located • Examples: DFS + data parallel runtime (Cosmos + Dryad, HDFS + Hadoop) • State oftheart: FAWN [SOSP ‘09] • Same trade-offs as previous case • Other considerations: • per-rack SSD cache • what if data-locality is not important?

  7. Case Study / Results • power proportional key-value stores and friends • knobs: • metadata (centralized or decentralized) • degree of replication • consistency model • workload model • service level objective • cost (hardware and electricity) cost power savings power savings latency / power savings power savings read latency write latency (strict consistency) write latency (eventual consistency) workload locality replication

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