Ghosts in the Machine: Interfaces for Better Power Management

1. Ghosts in the Machine:Interfaces for Better Power Management Manish Anand Edmund B. Nightingale Jason Flinn Electrical Engineering and Computer Science Dept. University of Michigan

2. Need for improved power management • Capabilities of mobile handheld devices improving rapidly: • Wireless connectivity • Storage capacity • Battery capacity improving slowly • I/O devices decrease handheld battery lifetime by 60% • OEMs provide techniques to reduce battery consumption by severely compromising the performance • Careful power management needed Manish Anand

3. Tradeoff between energy and performance Goals of power management involve: • Lower energy usage • Lower interactive response time (performance) Good performance Extended battery life Inherent tradeoff exists between performance and energy conservation Manish Anand

4. Current device power management • Problem: Device-centric strategies do not consider the operating environment contexts, such as • Base power of mobile computer • Activity of other devices • Application intent • Our Approach: Provide an infrastructure to expose additional information about operating environment to both applications and devices less more better performance and energy conservation MODULARITY simplicity Manish Anand

5. Goals and contributions • Goals: • Improve both performance and energy • Enable cross-device optimization • Allow end users to specify their relative priorities for performance and energy conservation • Contributions: Simple interfaces to expose additional contexts • Power manager for querying device state • Self-tuning power management(STPM) modules for disk, n/w • Ghost hints for cross-device power management Manish Anand

6. Outline • Motivation and background • Overview of our solution • Interfaces for better power management • Adaptive caching • Evaluation • Related work andconclusions Manish Anand

7. Review: power management • Network Device: 802.11b wireless • CAM – Continuously Active Mode • PSM – Power Saving Mode + power usage reduced by 70 to 80% - delay proportional to polling period (100 ms) • Disk Device: IBM/Hitachi microdrive • Active • Low Power Idle / Standby + power usage reduced by 55% / 90% - transition time of 300ms / 800ms Manish Anand

8. Impact of power management • Assumption: Fetching data from local storage is less costly • Local data access could be more expensive for small files • Break-even point is dependent on the state of the device • Simple adaptation through cache hierarchy Manish Anand

9. Limitation of adaptation Example: Browser fetching many small images • For single image fetching from network is correct • For many images fetching from disk is correct • Amortized transition cost over many reads • Problem: Reactive adaptive caching does not help • Disk continues to be in standby state • Solution: Apps disclose “accesses that might have been” • We use ghost hints to implement this Manish Anand

10. Outline • Motivation and background • Overview of our solution • Interfaces for better power management • Adaptive caching • Evaluation • Related work andconclusions Manish Anand

11. Energy-aware architecture Applications Cache Manager Data Access Decision Device Characteristics & Current State Ghost Hints Ghost Hints Network Hints STPM Network STPM Disk Power Manager MobiCom ‘03 Mode Transitions Mode Transitions Notify Network State Change Notify Disk State Change Network Device Driver Disk Device Driver Operating System Manish Anand

12. Power manager • Central repository that maintains information about: • State of the device • Performance characteristics of I/O devices • Energy characteristics of I/O devices • Power manager interface • 3 calls for devices – register, deregister and notify • 5 calls for applications – 4 to query device state and characteristics, 1 for registering callbacks Manish Anand

13. Self-tuning power management • STPM modules consider: • Application access patterns • Base power of mobile computer • Energy and performance characteristics of the device • Relative priority of performance and energy conservation • STPM modules decide: • Time to transition to high power state • Time to transition to low power state Energy conservation Performance 0 100 knob Manish Anand

14. Disk power management with STPM Transition to power saving mode employs • Break-even heuristic • Disk is likely to remain idle if it has been idle for a period • Incorporate the STPM principles Transition to active mode employs both • Reactive strategy • Transition on a request • Proactive strategy • Use ghost hints to transition even with no device access Manish Anand

15. Ghost hints GhostHint GhostHint Break-even Threshold state change on overflow Expended Energy between hints • Ghost hints contain opportunity cost in terms of time and energy • Issued by the application, when a poor I/O path is chosen due to inappropriate device state • Decrements energy cost of being active between hints • Transitions to high performance state on an overflow Manish Anand

16. Cache manager PowerManager device state Applications CacheStatus? Cache Manager ghost hint network better n/w fetch STPMModule PutData data flush Disk • Keeps the meta-data information in-memory hash table • Determines the weighted cost of going to network or disk • Maintains a write queue and delays writes to the disk Manish Anand

17. Outline • Motivation and background • Overview of our solution • Interfaces for better power management • Adaptive caching • Evaluation • Related work andconclusions Manish Anand

18. Evaluation • Goals: • Effect of cache manager’s adaptive, cross-device strategy • Influence of global knob • Benefits of ghost hints • Client: iPAQ handheld with Cisco 350 wireless card and IBM/Hitachi microdrive • Test applications: • Email-sync - recorded e-mail activity • Dillo web browser with wwwoffle - Berkeley web traces Manish Anand

19. Effectiveness of energy-aware caching • Equal priority for performance and energy • Average response time is reduced by 27% to 42% • Total energy usage is reduced by 5% to 9% Manish Anand

20. E-mail and web with no ghost hints • STPM modules base their decision solely on device accesses • No change for knob value less than 95 E-mail Web Manish Anand

21. Importance of ghost hints • Email: Substantial benefit when performance is high priority • Web: Less likely to see run of accesses clustered together E-mail Web Manish Anand

22. Importance of ghost hints Web with full cache • Ghost hints show a positive effect on the system • Ghost hints yield substantial benefit for some workloads, and do no harm in the situations where they seem ineffective Manish Anand

23. Related work • We are the first system to efficiently access data on multiple I/O • Burstiness for energy efficiency (Weissel ’02, Papathanasiou ’03, Heath ‘02) • ACPI: similar to power manager but too complex, does not disclose device characteristics • Wake on Wireless (Shih ’02) • OS level power management (ECOSystem, Odyssey) Manish Anand

24. Conclusions • Contributions: Improved power management interfaces: • Power manager for querying device state • STPM modules for disk, network • Ghost hints for cross-device power management • Current work: • Modification of cached data • Multiple network and storage devices • Feedback loop to dynamically set the global knob Manish Anand

25. Ghosts in the Machine:Interfaces for Better Power Management Manish Anand Edmund B. Nightingale Jason Flinn Electrical Engineering and Computer Science Dept. University of Michigan

26. Dynamic Power Management • The energy used by the I/O devices can be prohibitive, without power management • Battery lifetime is decreased by 60% without power management Drawback of using power management • Network device • Delay proportional to the polling period • Disk device • Transition cost to switch to active mode (300ms to 800ms) Manish Anand

27. Network access Disk access Applications STPM disk hints ghost hints Disk ghostrealized disk spin-up STPM network hints Network PSMswitch CAMswitch

28. Network Power Management with STPM • STPM network switches from PSM to CAM when: • Application specifies max delay < beacon period • Disclosed transfer size > break-even size • Many forthcoming transfers are likely • To predict forthcoming transfers STPM network generates an empirical distribution of run lengths Transfers >150ms >150 ms >150 ms Run Run Run Run Manish Anand