1. Energy Efficiency through Burstiness Athanasios E. Papathanasiou and Michael L. Scott University of Rochester, Computer Science Department Rochester, NY 14623, U.S.A. (WMCSA'03) 5th IEEE Workshop on Mobile Computing Systems and Applications Presented by Hsu Hao Chen

2. outline Introduction Design Guidelines Prototype Experimental Evaluation Conclusion

3. Introduction Smoothness: OS resource management policies traditionally employ buffering Maximize overall throughput and minimize the latency of individual requests. A little energy efficiency Burstiness: Can improve energy efficiency Without a significant impact on performance

4. Power-efficient devices Energy consumption parameters for various disks

5. Linux File System Behavior(1/2) CD copy (1359 sec) Disk Idle Time: 1191 seconds 92%: shorter then 5 seconds

6. Linux File System Behavior(2/2) MP3 playback (300 sec) Disk Idle Time: 292 seconds 66%: shorter than 8 seconds 6% only: longer than 16 second breakeven time

7. Disk Energy Savings vs. Memory Size Example: MPEG playback 64~496MB memory increase 3.4% disk energy savings

8. Design Guidelines(1/3) Maximize idle phases Aggressive, Speculative Prefetching With hints to improve accuracy Bursty Periodic Update Coordinating across applications Arrange for all applications to run out of data at the same time Prefetch daemon

9. Design Guidelines(2/3)

10. Design Guidelines(3/3) Maintaining responsiveness Responsiveness may be decreased because of Increased disk congestion due to burstiness Latency penalty for disk power-up Solution: Preactivate disk Monitor application progress and file system cache state Data consumption and production rates Initiate prefetch cycle before applications run out of data Interactive responsiveness Prioritized disk queues Required to service quickly unpredicted demand misses during periods of high disk congestion

11. Prototype(1/5) Epoch-based algorithm in the basic memory management mechanisms of the Linux kernel Each epoch consists of two phases: Request generation phase Idle phase

12. Prototype(2/5) Request generation phase Flush all dirty buffers Predict future data accesses Compute the amount of memory that can be used Prefetching Storing new data Free the required amount of memory Prefetch

13. Prototype(3/5) Idle phase Estimate time to next request Disk spin-down Predicted idle time is higher than the disk’s break-even time New epoch triggered by A new prefetching cycle has to be initiated A demand miss took place One or more dirty buffers have expired and it is time for them to be flushed Low system memory

14. Prototype(4/5) The Prefetch Cache Prefetch cache size Large enough to contain all predicted data accesses Without causing eviction of useful data Type of first miss determines prefetch cache size for next epoch Compulsory Miss Prefetch Miss Eviction Miss

15. Prototype(5/5) Update Policy Update daemon Flushes all dirty buffers once per minute. open Modified open system call Postpone write-behind until the file is closed Application without strict reliability constraints Examples: Copying of a file or MP3 encoding of a CD track

16. Experimental Evaluation(1/7) Dell Inspiron 4100 Laptop 512MB of memory Hitachi DK23DA hard disk Workload scenarios MPEG Playback (two 76MB files) Concurrent MPEG Playback and MP3 encoding MPEG Player Input: two 76MB files MP3 Encoder Input: 10 WAV files (626MB) MP3 Encoder Output: 10 MP3 files (42.9MB) Power management policy: Linux: 10 second fixed threshold Bursty: Predictive algorithm that monitors application progress and file system cache state

17. Experimental Evaluation(2/7) Cumulative distribution of disk idle time intervals during MPEG playback

18. Experimental Evaluation(3/7) Distribution of disk idle time intervals during concurrent MPEG playback and MP3 encoding

19. Experimental Evaluation(4/7)

20. Experimental Evaluation(5/7) execution of the MPEG player on Linux with 492MB

21. Experimental Evaluation(6/7) execution of the MPEG player on Bursty with 64MB and 128MB

22. Experimental Evaluation(7/7)

23. Conclusion Works well with predictable applications Energy savings scale with memory size Up to 78.5% disk energy savings Less than 5% performance penalty across all workloads and memory sizes