1. Semantic-less Coordination of Power Management and Application Performance HOTPOWER ‘09

2. About the Author(1)  Aman Kansal  Microsoft Research Redmond, WA

3. About the Author(2)  Jie Liu  Microsoft Research Redmond, WA

4. Abstract  Different power management modules affect each other.  Try to find out an approach for semantic-free coordination.  Design an interface at the system and application layers.

5. Semantic-less implies…  The values shared via the interface cannot be compared to other values.  A module only know it’s own values, don’t know whether a higher or lower value is better.  The goal is to compose multiple modules, with their independent strategies.

6. Contributions  A semantic-less mechanism: a narrow data interface and a generic coordination algorithm.  Applicaton tunes QoS, System changes P-state(processor voltage and frequency) without konwing anything about the other.

7. Related works  The coordination among system modules[6], application[3], and applications and system modules[11,12,8,2].  Joint optimizations of system and application performance[1,7].  All methods assume semantic information and the coordinated entities.

8. Power Performance Coordination

9. Coordinated system design(1)

10. Coordination interface  App(i) publishes QoS(i)  System modules publishes P(j), other modules don’t know what it means.(P-state, throughput cap, sleep mode, etc)  System also publishes a signal C in {-1, 0, 1} to indicate if energy needs to be reduced(c = -1) or keep constant(c = 0) or is available for increasing(c = 1).  (A system power measurement or estimate derived from performance counters based power models[5, 10] may be help determine whether power usage need to reduce)

11. Coordination Algorithm(1)  System algorithm

12. Application Algorithm(1)  If no module acquire the lock and the system determines hat energy usage must be reduced then is sets C = -1.  The action for C =1 is similar.

13. Application Algorithm(2)  After this, the system will detect at least one QoS(i) changed. The system then change i’s state. If the resource utilization reduced to a desired value and the system updates C = 0.  It will not supply C = 1 if previous configuration did not have the target power usage to prevent oscillations.

14. Experiment 1  Consider a battery operated laptop decoding high definition video. (on a Lenovo T61p)

15. Experiment 2(1)  This experiment considers multiple applications with different functionalities sharing a server.  A stream server serves HD (3.2Mbps), DVD(2Mbps), broadband (300kbps), dial-up(28 kbps.  Suppose the revenues (QoS levels) are $4, $3, $2, $0.5.  Varying CPU usage: 100%, 75%, 50% and 25%.  The conversion of searches to purchases varies with search quality leading to varying revenues of $6, $5, $4, and $1 respectively.  On an HPDLG380 blade server with 2x4-core Xeon processors and an 8-disk RAID array.

16. Experiment 2(2)

17. Reference  [1] P. Bodik, R. Griffith, C. Sutton, A. Fox, M. Jordan, and D. Patterson. Automatic exploration of datacenter performance regimes. In First Workshop on Automated Control for Datacenters and Clouds (ACDC09), Barcelona, Spain, June 2009.  [2] S. Brandt, G. Nutt, T. Berk, and J. Mankovich. A dynamic quality of service middleware agent for mediating application resource usage. In IEEE Real-Time Systems Symposium, December 1998.  [3] J. S. Chase, D. C. Anderson, P. N. Thakar, A. M. Vahdat, and R. P. Doyle. Managing energy and server resources in hosting centers. SIGOPS Oper. Syst. Rev., 35(5):103–116, 2001.  [4] D. D. Corkill. An introduction to blackboard systems. AI Expert, 6(9):40–47, September 1991. [5] X. Fan, W.-D. Weber, and L. A. Barroso. Power provisioning for a warehouse-sized computer. In Proceedings of the International Symposium on Computer Architecture (ISCA), June 2007.  [6] J. Heo, D. Henriksson, X. Liu, and T. Abdelzaher. Integrating adaptive components: An emerging challenge in performanceadaptive systems and a server farm case-study. In IEEE International Real-Time Systems Symposium, pages 227–238, 2007.  [7] X. Liu, P. Shenoy, and M. D. Corner. Chameleon: Application Level Power Management. IEEE Transactions on Mobile Computing, 7(8):995–1010, August 2008.  [8] R. Nathuji, P. England, P. Sharma, and A. Singh. Feedback driven qos-aware power budgeting for virtualized servers. In Feedback Control Implementation nd Design in Computing Systems and Networks (FeBID), San Francisco, CA, USA, April 2009.  [9] R. Raghavendra, P. Ranganathan, V. Talwar, Z. Wang, and X. Zhu. No ”power” struggles: coordinated multi-level power management for the data center. SIGOPS Oper. Syst. Rev., 42(2):48–59, 2008.  [10] S. Rivoire, P. Ranganathan, and C. Kozyrakis. A comparison of high-level full-system power models. In HotPower’08: Workshopon Power Aware Computing and Systems, December 2008.  [11] X.Wang and Y.Wang. Co-con: Coordinated control of power and application performance for virtualized server clusters. In 17 th IEEE International Workshop on Quality of Service (IWQoS), Charleston, South Carolina, July 2009.  [12] W. Yuan and K. Nahrstedt. Energy-efficient soft real-time cpu scheduling for mobile multimedia systems. In SOSP, October 2003.