Energy Efficient Prefetching – from models to Implementation

1. Energy Efficient Prefetching – from models to Implementation Adam Manzanares and Xiao Qin Department of Computer Science and Software Engineering Auburn Universityhttp://www.eng.auburn.edu/~xqin xqin@auburn.edu

2. Adam Manzanares Ph.D. May 2010.

3. About me Ph.D.’04, U. of Nebraska-Lincoln 04-07, New Mexico Tech 07-10, Auburn University

4. About My Research Group

5. Presentation Outline • Motivation • Modeling Work • DiskSim Modifications • Energy Efficient Virtual File System (EEVFS) • Parallel Striping Groups in EEVFS • Conclusion

6. Motivation EPA Report to Congress on Server and Data Center Energy Efficiency, 2007

7. Motivation • Using 2010 Historical 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

8. Energy-Related Reliability Model Prefetching Data Partitioning Security Model Disk Requests RAM Buffer Buffer Disk Controller Load Balancing Power Management m buffer disks n data disks Buffer Disk Architecture

9. IBM Ultrastar 36Z15

10. Prefetching Buffer Disk Disk 1 Disk 2 Disk 3

11. Why Modeling & Simulation • Allows us to determine the potential of our research ideas • Can quickly evaluate many simulation parameters • Allows us to test architectures and hardware without having the physical resources

12. Modeling & Simulation Work • Developed Mathematical Model • Disk Energy Consumption • Conditions to prefetch • Developed Energy Saving Principles • Investigated cases that exploit the energy saving principles • Implemented model in JAVA based simulator

13. Energy Saving Principles • Energy Saving Principle One • Increase the length and number of idle periods larger than the disk break-even time TBE • Energy Saving Principle Two • Reduce the number of power-state transitions

14. Paramaters Tested

15. Energy Savings Hit Rate 85%

16. State Transitions

17. Parameter Generalizations • Larger data sizes produce greater energy savings and less state transitions • Increasing the inter-arrival delay increases energy savings • More data disks per buffer disks increases energy efficiency • High hit rates produce the greatest energy efficiency

18. Modeling & Sim. Summary • Hit Rate, Inter-arrival Delay, & Data Size combine to produce Idle Windows • Transitions important to reduce energy consumption • May increase/decrease to reduce energy consumption • Disk parameters have large impact on energy savings • Model and simulator developed in-house

19. DiskSim • Event driven simulator developed at CMU • Simulates disks at the block level • The simulator has been validated • Discrete event based simulator • Provides a large amount of statistics • Lacks Disk Power Models • Ability to simulate large storage systems

20. File System Simulator • Large files important to energy savings • Popularity of data is also useful • Developed a block to file translator • Interacts with DiskSim

21. DiskSim with File System Simulator

22. Modified DiskSim Results

23. Modified DiskSim Summary • Provides us with accurate disk statistics • Only the changes to DiskSim need to be validated • Heavily dependent upon disk parameters • May miss details that can only be found in implementation

24. Why a Cluster File System • Block level prefetching difficult • Natural place to track file accesses • Control placement of data among storage nodes, and data disks • Tiered approach simplifies management of files and disk states • Eliminates some shortcomings of modeling and simulation

25. Energy Efficient Virtual File System

26. EEVFS Process Flow

27. EEVFS Testbed

28. Energy Savings

29. State Transitions

30. Response Times

31. Berkeley Web Trace

32. EEVFS Summary • Knowledge of requests assumed and may be hard to come by • Performance tied to one of the buffer disks

33. Parallel Striping Groups File 1 File 3 File 2 File 4 Group 1 Group 2 Buffer Disk Disk 1 Disk 2 Buffer Disk Disk 5 Disk 6 Storage Node 1 Storage Node 3 Buffer Disk Disk 3 Disk 4 Buffer Disk Disk 7 Disk 8 Storage Node 2 Storage Node 4

34. Striping Within a Group Buffer Disk Disk 1 Disk 2 1 2 3 5 7 9 4 6 8 10 Storage Node 1 Buffer Disk Disk 3 Disk 4 1 2 3 5 7 9 4 6 8 10 Storage Node 2 Group 1 File 2 2 2 1 File 1 1

35. Striping Within a Group • Number of disks in a group can be matched to nearest bottleneck • Striping within the group maintains relatively high performance • Allows us to use a buffer disk for each storage node, while still maintaining file striping level

36. Testbed

37. Measured Results

38. Measured Results

39. Berkeley Web Trace

40. Response Time Comparison • Energy efficiency is slightly improved • Response time gain is significant

41. Parallel Striping Groups Summary • Improves the energy efficiency and performance of a storage system • Designed to scale • Needs to be tested on large scale storage system

42. Conclusions • Modeling and simulation used to test our ideas • System, Disk, Trace Parameters varied to study their impacts • DiskSim Modifications • Added disk power models to DiskSim • Implemented block to file translator • Energy Aware Virtual Cluster File System (EEVFS) • Implemented a prototype • Added parallel striping groups to improve the energy efficiency

43. Future Work • Improve the EEVFS prototype for production use • Run EEVFS on large scale storage system • Investigate scaling effects

44. http://www.auburn.edu/~xzq0001

45. Download the presentation slides

46. Download the presentation slides

47. Download the presentation slides

48. http://www.slideshare.net/xqin74

49. Questions