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.

1. Energy-Efficient Parallel Storage Systems with Write-Buffer Disks XiaojunRuan and Xiao Qin Computer Science and Software Engineering Samuel Ginn College of Engineering Auburn University

2. My Research Group: 2011

3. Overview of the Project Energy Efficiency Security Solid State Drives Performance of Secure Disk Systems [IEEE NAS09] Design, Model, Simulate, And Evaluate Disk Systems with Buffer Disks [ACM SAC09][ICPP09] Enhancing Internal Parallelism of SSDs [To Be Submitted11] Message Passing Interface with Enhanced Security [IPCCC 2010] Energy-Efficient Distributed Storage Systems [IPCCC10] Energy-Efficient Dynamic Voltage Scaling[ICCCN07] Xiaojun Ruan 3 BUD

4. 4 6/10/2011 Electricity Usage in Data Centers Annual Data Center Electricity Usage and Electricity Price increase Every year

5. The average power consumption of TOP10 supercomputing systems is 1.32 Mwatt. Dell’s Texas Data Center 5 6/10/2011 Energy Efficiency of Supercomputers

7. Server and Data Centers Consume 110 Billion kWh per year

8. Assume average commercial end user is charged 9.46 kWh

9. Disk systems can account for 27% of the energy cost of data centers6/10/2011 6 Server and data centers may have an electrical cost of 10.4 billion dollars.

10. 7 6/10/2011 Energy Consumption of Disks

11. Power States of Disks Active State: high energy consumption Active Standby State transition penalty Standby State: low energy consumption 8

12. A Hard Disk Drive A10000RPM Hard Drive may take 10.9 seconds to wake up! 9

13. Parallel Disks Performance Energy Efficiency

17. Reduce Status Transitions as many as possible12

18. IBM Ultrastar 36Z15 6/10/2011 13

19. A Parallel Disk System with a Write Buffer Disk

20. The BUD Architecture Data Disks can serve requests without buffer disks when workload is high 15

22. SRB is set by administrators

24. Example Buffer Disk Requests Queue 18

25. Auburn University Xiaojun Ruan 19 From Design to Simulation

26. Simulation Environment 20

27. Auburn University 21 Workloads

28. Impact of SRB—Low Workload, UltraStar 22

29. Auburn University Xiaojun Ruan 23 Non-Buffer Experiments

30. Auburn University 24 BUD with IBM 40GNX TravalStar

31. Buffer Disk Number and Workload-- UltraStar 25

32. Auburn University 26 Energy Consumption E = Active Energy Consumption + Standby Energy Consumption + Transition Penalty

33. Auburn University Xiaojun Ruan 27 From Simulation to Real Implementation

34. An Energy-Efficient Cluster Storage System 28

35. Implementation (no buffer disks) 29

36. Implementation (with buffer-disks) 30

38. Data Disk 1: WesternDigital 400, 20GB

39. Data Disk 2: WesternDigital 400, 20GB

40. Disk Category I/O Node 2

41. Data Disk 1: WesternDigital 400, 20GB

42. Data Disk 2: Maxtor D740X-6L, 20GB31

44. Buffer Disk: Maxtor DiamondMax Plus 9

45. Data Disk 1: WesternDigital 400, 20GB

46. Data Disk 2: WesternDigital 400, 20GB

47. Disk Category I/O Node 2

48. Buffer Disk: Seagate Barracuda 7200

49. Data Disk 1: WesternDigital 400, 20GB

50. Data Disk 2: Maxtor D740X-6L, 20GB32

51. 33

52. 34 idle time gap is 200s idle time gap is 100s

53. Previous Research GreenFS [ACM EuroSys 2008] Massive Arrays of Idle Disks [SC 2002] Popular Data Concentration [ACM ICS 2004] 35 6/10/2011

54. Download the presentation slideshttp://www.slideshare.net/xqin74 Google: slideshare Xiao Qin ‹#›

55. http://www.eng.auburn.edu/~xqin

56. My webpagehttp://www.eng.auburn.edu/~xqin

57. Download Slides at slidesharehttp://www.slideshare.net/xqin74

58. Auburn University 40 Questions?