Power-aware resource allocation in high-end systems via online simulation

ABSTRACT

Traditionally, scheduling in high-end parallel systems focuses on how to minimize the average job waiting time and on how to maximize the overall system utilization. Despite the development of scheduling strategies that aim at maximizing system utilization, parallel supercomputing traces that span long time periods indicate that such systems are mostly underutilized. Much of the time there is simply not enough load to keep the system fully utilized, although time periods do exist where system utilization levels peak at nearly 95%. In this paper, we propose a new family of scheduling policies that aims at minimizing power consumption and cooling costs by selectively choosing to power down (or put in "sleep" mode) parts of the system during periods of low load. Our goal is the development of a scheduling mechanism that adaptively adjusts the number of processors to the offered load while meeting predefined service-level agreements (SLAs). This scheduling mechanism uses online simulation, i.e., lightweight simulation modules that can execute while the system and its scheduler are in operation, and can guide resource provisioning in parallel systems. Detailed experimentation using traces from the Parallel Workloads Archive indicates that the proposed online mechanism is a viable alternative to conserve energy while meeting performance-based SLAs.

REFERENCES

• 1: Jeffrey S. Chase , Darrell C. Anderson , Prachi N. Thakar , Amin M. Vahdat , Ronald P. Doyle, Managing energy and server resources in hosting centers, Proceedings of the eighteenth ACM symposium on Operating systems principles, October 21-24, 2001, Banff, Alberta, Canada
• 2: R. Doyle, Energy Management for Server Clusters, Proceedings of the Eighth Workshop on Hot Topics in Operating Systems, p.165, May 20-22, 2001
• 3: E. N. Elnozahy, M. Kistler, and R. Rajamony. Energy-efficient server clusters. In Proceedings of the Second Workshop on Power Aware Computing Systems, pages 179--196, 2002.
• 4: E. N. Elnozahy, M. Kistler, and R. Rajamony. Energy conservation policies for web servers. In Proceedings of USENIX Symposium on Internet Technologies and Systems, USITS'03, 2003.
• 5: Dror G. Feitelson , Larry Rudolph, Parallel Job Scheduling: Issues and Approaches, Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing, p.1-18, April 25, 1995
• 6: Parallel Workload Archive. http://www.cs.huji.ac.il/labs/parallel/workload/.
• 7: IBM LoadLeveler. http://publib.boulder.ibm.com/clresctr/windows/public/llbooks.html.
• 8: David B. Jackson , Quinn Snell , Mark J. Clement, Core Algorithms of the Maui Scheduler, Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing, p.87-102, June 16, 2001
• 9: Peter J. Keleher , Dmitry Zotkin , Dejan Perkovic, Attacking the bottlenecks of backfilling schedulers, Cluster Computing, v.3 n.4, p.245-254, 2000 [doi>10.1023/A:1019044623636]
• 10: Barry G. Lawson , Evgenia Smirni, Multiple-Queue Backfilling Scheduling with Priorities and Reservations for Parallel Systems, Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing, p.72-87, July 24, 2002
• 11: Barry Lawson , Evgenia Smirni, Self-Adaptive Scheduler Parameterization via Online Simulation, Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers, p.29.1, April 04-08, 2005 [doi>10.1109/IPDPS.2005.398]
• 12: C. B. Lee, Y. Schwartzman, J. Hardy, and A. Snavely. Are user runtime estimates inherently inaccurate? In Job Scheduling Strategies for Parallel Processing (JSSPP 2004), pages 253--263, Springer-Verlag, 2004. LNCS vol. 3277.
• 13: Maui Scheduler Open Cluster Software. http://mauischeduler.sourceforge.net/.
• 14: Ahuva W. Mu'alem , Dror G. Feitelson, Utilization, Predictability, Workloads, and User Runtime Estimates in Scheduling the IBM SP2 with Backfilling, IEEE Transactions on Parallel and Distributed Systems, v.12 n.6, p.529-543, June 2001 [doi>10.1109/71.932708]
• 15: Portable Batch System. http://www.openpbs.org/.
• 16: Dejan Perkovic , Peter J. Keleher, Randomization, speculation, and adaptation in batch schedulers, Proceedings of the 2000 ACM/IEEE conference on Supercomputing (CDROM), p.7-es, November 04-10, 2000, Dallas, Texas, United States
• 17: Vivek Sharma , Arun Thomas , Tarek Abdelzaher , Kevin Skadron , Zhijian Lu, Power-aware QoS Management in Web Servers, Proceedings of the 24th IEEE International Real-Time Systems Symposium, p.63, December 03-05, 2003
• 18: David Talby , Dror G. Feitelson, Supporting Priorities and Improving Utilization of the IBM SP Scheduler Using Slack-Based Backfilling, Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing, p.513, April 12-16, 1999
• 19: Bhuvan Urgaonkar , Prashant Shenoy, Brief announcement: Cataclysm: handling extreme overloads in internet services, Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing, July 25-28, 2004, St. John's, Newfoundland, Canada [doi>10.1145/1011767.1011851]
• 20: Yanyong Zhang , Hubertus Franke , Jose Moreira , Anand Sivasubramaniam, An Integrated Approach to Parallel Scheduling Using Gang-Scheduling, Backfilling, and Migration, IEEE Transactions on Parallel and Distributed Systems, v.14 n.3, p.236-247, March 2003 [doi>10.1109/TPDS.2003.1189582]

INDEX TERMS

Keywords:
online simulation, parallel workload characterization, performance evaluation, power-aware scheduling, resource allocation