Energy Efficient Prefetching – from Models to Implementation


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With the rapid growth of the production and storage of large scale data sets it is important to investigate methods to drive the cost of storage systems down. We are currently in the midst of an information explosion and large scale storage centers are increasingly used to help store generated data. There are several methods to bring the cost of large scale storage centers down and we investigate a technique that focuses on transitioning storage disks into lower power states. This talk introduces a model of disk systems that leverages disk access patterns to prefetch popular sets of data to produce energy saving opportunities. Using the model we have developed a simulator that allows us to quickly change various parameters to investigate the relationship that file access patterns, disk energy parameters, and simulation parameters have on the overall energy efficiency of disk systems. To help improve the validity of our simulation results we leveraged the validated disk simulator, DiskSim, and added disk power models to DiskSim. This allowed us to test our energy efficient strategies with a validated storage system simulator.

The last part of this talk focuses on implementing a large scale storage system virtual file system. We introduce the Energy Efficient Virtual File System, or EEVFS, to mange the data placement and disk states in a cluster storage system. Our modeling and simulation results indicated that large data sizes and knowledge about the disk access pattern are valuable for storage system energy savings techniques. Storage servers that support applications that stream media is one key area that would benefit from our strategies. The final idea introduced in this talk is the concept of parallel striping groups, which attempt to improve the performance of EEVFS while maintaining energy savings.

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  • Web Servers they account for 24% of the energy, 77% proxy server, 27% in data center
  • 12 disk
  • Energy Efficient Prefetching – from Models to Implementation

    1. 1. Energy Efficient Prefetching – from models to Implementation 05/25/10 Adam Manzanares and Xiao Qin Department of Computer Science and Software Engineering Auburn University [email_address]
    2. 2. Adam Manzanares Ph.D. May 2010.
    3. 3. About me Ph.D.’04, U. of Nebraska-Lincoln 04-07, New Mexico Tech 07-10, Auburn University
    4. 4. About My Research Group
    5. 5. Presentation Outline <ul><li>Motivation </li></ul><ul><li>Modeling Work </li></ul><ul><li>DiskSim Modifications </li></ul><ul><li>Energy Efficient Virtual File System (EEVFS) </li></ul><ul><li>Parallel Striping Groups in EEVFS </li></ul><ul><li>Conclusion </li></ul>05/25/10
    6. 6. Motivation EPA Report to Congress on Server and Data Center Energy Efficiency, 2007 05/25/10
    7. 7. Motivation <ul><li>Using 2010 Historical Trends Scenario </li></ul><ul><ul><li>Server and Data Centers Consume 110 Billion kWh per year </li></ul></ul><ul><ul><li>Assume average commercial end user is charged 9.46 kWh </li></ul></ul><ul><ul><li>Disk systems can account for 27% of the energy cost of data centers </li></ul></ul>05/25/10
    8. 8. Buffer Disk Architecture 05/25/10 RAM Buffer m buffer disks n data disks Buffer Disk Controller Data Partitioning Security Model Load Balancing Power Management Prefetching Disk Requests Energy-Related Reliability Model
    9. 9. IBM Ultrastar 36Z15 05/25/10 Transfer Rate 55 MB/s Spin Down Time: T D 1.5 s Active Power: P A 13.5 W Spin Up Time: T U 10.9 s Idle Power: P I 10.2 W Spin Down Energy: E D 13 J Standby Power: P A 2.5 W Spin Up Energy: E U 135 J Break-Even Time: T BE 15.2 S
    10. 10. Prefetching Disk 1 Disk 2 Disk 3 Buffer Disk 05/25/10
    11. 11. Why Modeling & Simulation <ul><li>Allows us to determine the potential of our research ideas </li></ul><ul><li>Can quickly evaluate many simulation parameters </li></ul><ul><li>Allows us to test architectures and hardware without having the physical resources </li></ul>05/25/10
    12. 12. Modeling & Simulation Work <ul><li>Developed Mathematical Model </li></ul><ul><ul><li>Disk Energy Consumption </li></ul></ul><ul><ul><li>Conditions to prefetch </li></ul></ul><ul><li>Developed Energy Saving Principles </li></ul><ul><ul><li>Investigated cases that exploit the energy saving principles </li></ul></ul><ul><li>Implemented model in JAVA based simulator </li></ul>05/25/10
    13. 13. Energy Saving Principles <ul><li>Energy Saving Principle One </li></ul><ul><ul><li>Increase the length and number of idle periods larger than the disk break-even time T BE </li></ul></ul><ul><li>Energy Saving Principle Two </li></ul><ul><ul><li>Reduce the number of power-state transitions </li></ul></ul>05/25/10
    14. 14. Paramaters Tested 05/25/10 Parameter Values Data Size 1, 5, 10, 25 MB # of Data Disks 4, 8, 12 Inter-arrival Delay 0, 0.1, 0.5, 1 S Hit Rate 85, 90, 95, 100%
    15. 15. Energy Savings Hit Rate 85% 05/25/10
    16. 16. State Transitions 05/25/10
    17. 17. Parameter Generalizations <ul><li>Larger data sizes produce greater energy savings and less state transitions </li></ul><ul><li>Increasing the inter-arrival delay increases energy savings </li></ul><ul><li>More data disks per buffer disks increases energy efficiency </li></ul><ul><li>High hit rates produce the greatest energy efficiency </li></ul>05/25/10
    18. 18. Modeling & Sim. Summary <ul><li>Hit Rate, Inter-arrival Delay, & Data Size combine to produce Idle Windows </li></ul><ul><li>Transitions important to reduce energy consumption </li></ul><ul><ul><li>May increase/decrease to reduce energy consumption </li></ul></ul><ul><li>Disk parameters have large impact on energy savings </li></ul><ul><li>Model and simulator developed in-house </li></ul>05/25/10
    19. 19. DiskSim <ul><li>Event driven simulator developed at CMU </li></ul><ul><li>Simulates disks at the block level </li></ul><ul><li>The simulator has been validated </li></ul><ul><li>Discrete event based simulator </li></ul><ul><li>Provides a large amount of statistics </li></ul><ul><li>Lacks Disk Power Models </li></ul><ul><li>Ability to simulate large storage systems </li></ul>05/25/10
    20. 20. File System Simulator <ul><li>Large files important to energy savings </li></ul><ul><li>Popularity of data is also useful </li></ul><ul><li>Developed a block to file translator </li></ul><ul><li>Interacts with DiskSim </li></ul>05/25/10
    21. 21. DiskSim with File System Simulator 05/25/10
    22. 22. Modified DiskSim Results 05/25/10
    23. 23. Modified DiskSim Summary <ul><li>Provides us with accurate disk statistics </li></ul><ul><li>Only the changes to DiskSim need to be validated </li></ul><ul><li>Heavily dependent upon disk parameters </li></ul><ul><li>May miss details that can only be found in implementation </li></ul>05/25/10
    24. 24. Why a Cluster File System <ul><li>Block level prefetching difficult </li></ul><ul><li>Natural place to track file accesses </li></ul><ul><li>Control placement of data among storage nodes, and data disks </li></ul><ul><li>Tiered approach simplifies management of files and disk states </li></ul><ul><li>Eliminates some shortcomings of modeling and simulation </li></ul>05/25/10
    25. 25. Energy Efficient Virtual File System 05/25/10
    26. 26. EEVFS Process Flow 05/25/10
    27. 27. EEVFS Testbed 05/25/10 Parameter Storage Server Storage Node Type 1 Storage Node Type 2 CPU P4 2.0 GHz P4 3.2 GHz P4 2.4 GHz Memory (MB) 2000 1000 512 Network Interconnect 1000 1000 100 Disk Type SATA ATA/133 ATA/133 Disk Capacity 120 GB 80 GB 80 GB Disk Bandwidth 100 MB/s 58 MB/s 34 MB/s
    28. 28. Energy Savings 05/25/10
    29. 29. State Transitions 05/25/10
    30. 30. Response Times 05/25/10
    31. 31. Berkeley Web Trace 05/25/10
    32. 32. EEVFS Summary <ul><li>Knowledge of requests assumed and may be hard to come by </li></ul><ul><li>Performance tied to one of the buffer disks </li></ul>05/25/10
    33. 33. Parallel Striping Groups Group 1 Storage Node 1 Storage Node 2 Group 2 Storage Node 3 Storage Node 4 File 1 File 2 File 3 File 4 05/25/10 Disk 1 Disk 2 Buffer Disk Disk 3 Disk 4 Buffer Disk Disk 5 Disk 6 Buffer Disk Disk 7 Disk 8 Buffer Disk
    34. 34. Striping Within a Group Group 1 Storage Node 1 Storage Node 2 1 3 5 7 9 4 6 8 4 6 8 1 3 5 7 9 10 10 1 2 1 2 File 1 File 2 2 2 05/25/10 Disk 1 Disk 2 Buffer Disk Disk 3 Disk 4 Buffer Disk
    35. 35. Striping Within a Group <ul><li>Number of disks in a group can be matched to nearest bottleneck </li></ul><ul><li>Striping within the group maintains relatively high performance </li></ul><ul><li>Allows us to use a buffer disk for each storage node, while still maintaining file striping level </li></ul>05/25/10
    36. 36. Testbed 05/25/10 Parameter Storage Server Storage Node CPU Celeron 2.2 GHz Celeron 2.2 GHz Memory (MB) 2000 2000 Network Interconnect 1000 1000 Disk Type SATA SATA Disk Capacity 160 GB 480 GB Disk Bandwidth 126 MB/s 126 MB/s
    37. 37. Measured Results 05/25/10
    38. 38. Measured Results 05/25/10
    39. 39. Berkeley Web Trace 05/25/10
    40. 40. Response Time Comparison <ul><li>Energy efficiency is slightly improved </li></ul><ul><li>Response time gain is significant </li></ul>05/25/10 Parameter Striping No Striping Energy Consumption (J) 2,088,113 2,100,243 Response Time (S) 2.78 13.87
    41. 41. Parallel Striping Groups Summary <ul><li>Improves the energy efficiency and performance of a storage system </li></ul><ul><li>Designed to scale </li></ul><ul><ul><li>Needs to be tested on large scale storage system </li></ul></ul>05/25/10
    42. 42. Conclusions <ul><li>Modeling and simulation used to test our ideas </li></ul><ul><ul><li>System, Disk, Trace Parameters varied to study their impacts </li></ul></ul><ul><li>DiskSim Modifications </li></ul><ul><ul><li>Added disk power models to DiskSim </li></ul></ul><ul><ul><li>Implemented block to file translator </li></ul></ul><ul><li>Energy Aware Virtual Cluster File System (EEVFS) </li></ul><ul><ul><li>Implemented a prototype </li></ul></ul><ul><ul><li>Added parallel striping groups to improve the energy efficiency </li></ul></ul>05/25/10
    43. 43. Future Work <ul><li>Improve the EEVFS prototype for production use </li></ul><ul><li>Run EEVFS on large scale storage system </li></ul><ul><ul><li>Investigate scaling effects </li></ul></ul>05/25/10
    44. 44.
    45. 45. Download the presentation slides
    46. 46. Download the presentation slides
    47. 47. Download the presentation slides
    48. 48. Questions