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Sierra: towards power-proportionality for data-intensive data centers

Sierra: towards power-proportionality for data-intensive data centers. Eno Thereska, Austin Donnelly, Dushyanth Narayanan, Natasa Milic-Frayling Microsoft Research Cambridge Systems & Networking. Saumil Rampal Imperial College London Business School (intern at Microsoft Research).

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Sierra: towards power-proportionality for data-intensive data centers

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  1. Sierra: towards power-proportionality for data-intensive data centers Eno Thereska, Austin Donnelly, Dushyanth Narayanan, Natasa Milic-Frayling Microsoft Research Cambridge Systems & Networking SaumilRampal Imperial College London Business School (intern at Microsoft Research)

  2. Our Aim • To ensure that our development effort is in synchronization with the requirements of our valued partners and customers.

  3. Do your workloads look like these? load Hotmail ~ 100,000 Servers Messenger ~ 10,000 servers days MSR-C ~ 100 servers Servers not fully utilized Zero-load server draws ~60% of fully loaded server!

  4. Power-proportionality in software • Turn off servers, rebalance CPU and I/O load • CPU & network state can be migrated (VMs) • Storage state can not be migrated • Terabytes per server, petabytes per DC • Diurnal patterns  migrate at least twice a day! • Turn servers off, but keep data available? • and consistent, and fault-tolerant

  5. Sierra in a nutshell • Exploit R-way replication for read availability • Careful layout to maximize #servers in standby • Short term, versioned store for write availability & read/write consistency • Good power savings • Hotmail sample: 28-50% • Messenger: 19% • MSR-C: 60%

  6. Context: Types of Solutions for Optimizing Energy in Data Centers • Building Designs • Cooling Systems • Power Systems • Sources of Power • Efficient Hardware • Optimization Software

  7. Optimization Software • 1. Plan for the peak load of the data centre. • 2. Traffic Shaping. • 3. Scheduled or interactive background batch processes . • 4. Power Proportionality Customer load (e.g., IOPS/second) Time

  8. Configuration of Servers Existing Virtual machine consolidation solutions Sierra + Existing Virtual machine consolidation solutions Sierra + Existing Virtual machine consolidation solutions

  9. Summary • A power efficient solution for data-intensive (CPU + storage) data centers • Power savings in software through server control Technical resources: http://research.microsoft.com/sierra Email: etheres@microsoft.com

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