Energy-Efficient Parallel Storage Systems with Write-Buffer Disks<br />XiaojunRuan and Xiao Qin<br />Computer Science and ...
My Research Group: 2011<br />
Overview of the Project<br />Energy Efficiency<br />Security<br />Solid State Drives<br />Performance of <br />Secure Disk...
4<br />6/10/2011<br />Electricity Usage in Data Centers<br />Annual Data Center Electricity Usage and Electricity Price in...
The average power consumption of TOP10 supercomputing systems is 1.32 Mwatt.<br />Dell’s Texas Data Center<br />5<br />6/1...
Electrical Cost of Data Centers<br /><ul><li>Using 2010 Trends Scenario
Server and Data Centers Consume 110 Billion kWh per year
Assume average commercial end user is charged 9.46 kWh
Disk systems can account for 27% of the energy cost of data centers</li></ul>6/10/2011<br />6<br />Server and data centers...
7<br />6/10/2011<br />Energy Consumption of Disks<br />
Power States of Disks<br />   Active State: high energy consumption<br />Active<br />Standby<br />State transition penalty...
A Hard Disk Drive<br />A10000RPM Hard Drive may take 10.9 seconds to wake up!<br />9<br />
Parallel Disks<br />Performance<br />Energy Efficiency<br />
Challanges<br />Performance Oriented:<br /><ul><li>  Best Performance
 Huge Electricity Bills</li></ul> Energy Efficiency Oriented:<br /><ul><li>  Worst Performance?
 Small Electricity Bills</li></li></ul><li>Basic Idea of BUD<br /><ul><li>Keep Disks in Standby mode as long as possible
Reduce Status Transitions as many as possible</li></ul>12<br />
IBM Ultrastar 36Z15<br />6/10/2011<br />13<br />
A Parallel Disk System with a Write Buffer Disk<br />
The BUD Architecture<br />Data Disks can serve requests without buffer disks when workload is high<br />15<br />
Auburn University	<br />16<br />Sum of Requests in Buffer (SRB)<br /><ul><li>SRB is Number of the buffered requests target...
SRB is set by administrators
Once SRB is satisfied, spin up the targeted data disk, dump all those data, then spin the disk down.</li></li></ul><li>Sch...
Example<br />Buffer Disk<br />Requests Queue<br />18<br />
Auburn University<br /> Xiaojun Ruan<br />19<br />From Design to Simulation<br />
Simulation Environment<br />20<br />
Auburn University	<br />21<br />Workloads<br />
Impact of SRB—Low Workload, UltraStar<br />22<br />
Auburn University	<br />Xiaojun Ruan<br />23<br />Non-Buffer Experiments<br />
Auburn University	<br />24<br />BUD with IBM 40GNX TravalStar<br />
Buffer Disk Number and Workload-- UltraStar<br />25<br />
Auburn University	<br />26<br />Energy Consumption<br />E = Active Energy Consumption + Standby Energy Consumption + Trans...
Auburn University<br /> Xiaojun Ruan<br />27<br />From  Simulation to <br />Real Implementation<br />
An Energy-Efficient Cluster Storage System<br />28<br />
Implementation (no buffer disks)<br />29<br />
Implementation (with buffer-disks)<br />30<br />
Experimental Design<br /><ul><li>Disk Category I/O Node 1
Data Disk 1: WesternDigital 400, 20GB
Data Disk 2: WesternDigital 400, 20GB
Disk Category I/O Node 2
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BUDW: Energy-Efficient Parallel Storage Systems with Write-Buffer Disks

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A critical challenge with modern parallel I/O systems is that parallel disks consume a significant amount of energy in servers and high-performance computers. To conserve energy consumption in parallel I/O systems, one can immediately spin down disks when disk are idle; however, spinning down disks might not be able to produce energy savings due to penalties of spinning operations. Unlike powering up CPUs, spinning down and up disks need physical movements. Therefore, energy savings provided by spinning down operations must offset energy penalties of the disk spinning operations. To reduce the penalties incurred by disk spinning operations, we describe in this talk an approach to conserving energy of parallel I/O systems with write buffer disks, which are used to accumulate small writes using a log file system. Data sets buffered in the log file system can be transferred to target data disks in a batch way. Thus, buffer disks aim to serve a majority of incoming write requests, attempting to reduce the large number of disk spinning operations by keeping data disks in standby for long period times. Interestingly, the write buffer disks not only can achieve high energy efficiency in parallel I/O systems, but also can shorten response times of write requests. To evaluate the performance and energy efficiency of our parallel I/O systems with buffer disks, we implemented a prototype using a cluster storage system as a testbed. Experimental results show that under light and moderate I/O load, buffer disks can be employed to significantly reduce energy dissipation in parallel I/O systems without adverse impacts on I/O performance.

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  • Web Servers they account for 24% of the energy, 77% proxy server, 27% in data center
  • , then spin the data disk on, otherwise we always keep data disks in standby mode.
  • SRB is small, transfer data from buffer disk to data disk for too many times. Small SRB, more spin up and spin down times.
  • Trend is similar, but ultrastar penalty is higher
  • MAID:GreenFS:Benefits: low energy cost, Flaws: low capacity, low reliability
  • BUDW: Energy-Efficient Parallel Storage Systems with Write-Buffer Disks

    1. 1. Energy-Efficient Parallel Storage Systems with Write-Buffer Disks<br />XiaojunRuan and Xiao Qin<br />Computer Science and Software Engineering<br />Samuel Ginn College of Engineering<br />Auburn University<br />
    2. 2. My Research Group: 2011<br />
    3. 3. Overview of the Project<br />Energy Efficiency<br />Security<br />Solid State Drives<br />Performance of <br />Secure Disk Systems<br />[IEEE NAS09]<br />Design, Model, <br />Simulate, And Evaluate <br />Disk Systems with <br />Buffer Disks<br />[ACM SAC09][ICPP09]<br />Enhancing Internal <br />Parallelism of SSDs<br />[To Be Submitted11]<br />Message Passing <br />Interface with <br />Enhanced Security<br />[IPCCC 2010]<br />Energy-Efficient<br />Distributed Storage<br />Systems [IPCCC10]<br />Energy-Efficient <br />Dynamic Voltage <br />Scaling[ICCCN07]<br /> Xiaojun Ruan<br />3<br />BUD<br />
    4. 4. 4<br />6/10/2011<br />Electricity Usage in Data Centers<br />Annual Data Center Electricity Usage and Electricity Price increase Every year<br />
    5. 5. The average power consumption of TOP10 supercomputing systems is 1.32 Mwatt.<br />Dell’s Texas Data Center<br />5<br />6/10/2011<br />Energy Efficiency of Supercomputers<br />
    6. 6. Electrical Cost of Data Centers<br /><ul><li>Using 2010 Trends Scenario
    7. 7. Server and Data Centers Consume 110 Billion kWh per year
    8. 8. Assume average commercial end user is charged 9.46 kWh
    9. 9. Disk systems can account for 27% of the energy cost of data centers</li></ul>6/10/2011<br />6<br />Server and data centers may have an electrical cost of 10.4 billion dollars.<br />
    10. 10. 7<br />6/10/2011<br />Energy Consumption of Disks<br />
    11. 11. Power States of Disks<br /> Active State: high energy consumption<br />Active<br />Standby<br />State transition penalty<br /> Standby State: low energy consumption<br />8<br />
    12. 12. A Hard Disk Drive<br />A10000RPM Hard Drive may take 10.9 seconds to wake up!<br />9<br />
    13. 13. Parallel Disks<br />Performance<br />Energy Efficiency<br />
    14. 14. Challanges<br />Performance Oriented:<br /><ul><li> Best Performance
    15. 15. Huge Electricity Bills</li></ul> Energy Efficiency Oriented:<br /><ul><li> Worst Performance?
    16. 16. Small Electricity Bills</li></li></ul><li>Basic Idea of BUD<br /><ul><li>Keep Disks in Standby mode as long as possible
    17. 17. Reduce Status Transitions as many as possible</li></ul>12<br />
    18. 18. IBM Ultrastar 36Z15<br />6/10/2011<br />13<br />
    19. 19. A Parallel Disk System with a Write Buffer Disk<br />
    20. 20. The BUD Architecture<br />Data Disks can serve requests without buffer disks when workload is high<br />15<br />
    21. 21. Auburn University <br />16<br />Sum of Requests in Buffer (SRB)<br /><ul><li>SRB is Number of the buffered requests targetingat the same data disk.
    22. 22. SRB is set by administrators
    23. 23. Once SRB is satisfied, spin up the targeted data disk, dump all those data, then spin the disk down.</li></li></ul><li>Scheduling Strategy<br />To buffer enough requests targeting at the same data disk<br />DynAmic Request Allocation algorithm for Writes<br />17<br />
    24. 24. Example<br />Buffer Disk<br />Requests Queue<br />18<br />
    25. 25. Auburn University<br /> Xiaojun Ruan<br />19<br />From Design to Simulation<br />
    26. 26. Simulation Environment<br />20<br />
    27. 27. Auburn University <br />21<br />Workloads<br />
    28. 28. Impact of SRB—Low Workload, UltraStar<br />22<br />
    29. 29. Auburn University <br />Xiaojun Ruan<br />23<br />Non-Buffer Experiments<br />
    30. 30. Auburn University <br />24<br />BUD with IBM 40GNX TravalStar<br />
    31. 31. Buffer Disk Number and Workload-- UltraStar<br />25<br />
    32. 32. Auburn University <br />26<br />Energy Consumption<br />E = Active Energy Consumption + Standby Energy Consumption + Transition Penalty<br />
    33. 33. Auburn University<br /> Xiaojun Ruan<br />27<br />From Simulation to <br />Real Implementation<br />
    34. 34. An Energy-Efficient Cluster Storage System<br />28<br />
    35. 35. Implementation (no buffer disks)<br />29<br />
    36. 36. Implementation (with buffer-disks)<br />30<br />
    37. 37. Experimental Design<br /><ul><li>Disk Category I/O Node 1
    38. 38. Data Disk 1: WesternDigital 400, 20GB
    39. 39. Data Disk 2: WesternDigital 400, 20GB
    40. 40. Disk Category I/O Node 2
    41. 41. Data Disk 1: WesternDigital 400, 20GB
    42. 42. Data Disk 2: Maxtor D740X-6L, 20GB</li></ul>31<br />
    43. 43. Experimental Design<br /><ul><li>Disk Category I/O Node 1
    44. 44. Buffer Disk: Maxtor DiamondMax Plus 9
    45. 45. Data Disk 1: WesternDigital 400, 20GB
    46. 46. Data Disk 2: WesternDigital 400, 20GB
    47. 47. Disk Category I/O Node 2
    48. 48. Buffer Disk: Seagate Barracuda 7200
    49. 49. Data Disk 1: WesternDigital 400, 20GB
    50. 50. Data Disk 2: Maxtor D740X-6L, 20GB</li></ul>32<br />
    51. 51. 33<br />
    52. 52. 34<br />idle time gap is 200s<br />idle time gap is 100s<br />
    53. 53. Previous Research<br />GreenFS<br />[ACM EuroSys 2008]<br />Massive Arrays of Idle Disks<br />[SC 2002]<br />Popular Data Concentration<br />[ACM ICS 2004]<br />35<br />6/10/2011<br />
    54. 54. Download the presentation slideshttp://www.slideshare.net/xqin74<br />Google: slideshare Xiao Qin<br />‹#› <br />
    55. 55. http://www.eng.auburn.edu/~xqin<br />
    56. 56. My webpagehttp://www.eng.auburn.edu/~xqin<br />
    57. 57. Download Slides at slidesharehttp://www.slideshare.net/xqin74<br />
    58. 58. Auburn University<br />40<br />Questions?<br />

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