Autonomic multi-agent management of power and performance in data centers

ABSTRACT

The rapidly rising cost and environmental impact of energy consumption in data centers has become a multi-billion dollar concern globally. In response, the IT Industry is actively engaged in a first-to-market race to develop energy-conserving hardware and software solutions that do not sacrifice performance objectives. In this work we demonstrate a prototype of an integrated data center power management solution that employs server management tools, appropriate sensors and monitors, and an agent-based approach to achieve specified power and performance objectives. By intelligently turning off servers under low-load conditions, we can achieve over 25% power savings over the unmanaged case without incurring SLA penalties for typical daily and weekly periodic demands seen in webserver farms.

REFERENCES

• 1: P. Abbeel et al. An application of reinforcement learning to aerobatic helicopter flight. In Proc. of NIPS-06, 2006.
• 2: N. Bobroff, A. Kochut, and K. Beaty. Dynamic placement of virtual machines for managing sla violations. In Integrated Network Management, pages 119--128, 2007.
• 3: Rajarshi Das , Ian Whalley , Jeffrey O. Kephart, Utility-based collaboration among autonomous agents for resource allocation in data centers, Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems, May 08-12, 2006, Hakodate, Japan [doi>10.1145/1160633.1160935]
• 4: Gartner Inc. Gartner Says 50 Percent of Data Centers Will Have Insufficient Power and Cooling Capacity by 2008. Press Release, November 29, 2006.
• 5: Carlos Guestrin , Michail G. Lagoudakis , Ronald Parr, Coordinated Reinforcement Learning, Proceedings of the Nineteenth International Conference on Machine Learning, p.227-234, July 08-12, 2002
• 6: IBM Corp. IBM Director Agent Extensions: Active Energy Manager. http://www-03.ibm.com/systems/management/director/extensions/actengmrg.html, 2007.
• 7: Intel Corp. Intel Math Kernel Library 10.0 - LINPACK. http://www.intel.com/cd/software/products/asmo-na/eng/266857.htm, 2007.
• 8: Intel Corp. et al. Intelligent platform management interface specification v2.0. http://www.intel.com/design/servers/ipmi/pdf/IPMIv2_0_rev1_0_E3_markup.pdf, 2006.
• 9: Jeffrey O. Kephart , Hoi Chan , Rajarshi Das , David W. Levine , Gerald Tesauro , Freeman Rawson , Charles Lefurgy, Coordinating Multiple Autonomic Managers to Achieve Specified Power-Performance Tradeoffs, Proceedings of the Fourth International Conference on Autonomic Computing, p.24, June 11-15, 2007 [doi>10.1109/ICAC.2007.12]
• 10: J. G. Koomey. Estimating total power consumption by servers in the U.S. and the world. http://enterprise.amd.com/Downloads/svrpwrusecompletefinal.pdf, 2007.
• 11: Lawrence Berkeley National Laboratory. The internet traffic achive. http://ita.ee.lbl.gov/, 2005.
• 12: Charles Lefurgy , Xiaorui Wang , Malcolm Ware, Server-Level Power Control, Proceedings of the Fourth International Conference on Autonomic Computing, p.4, June 11-15, 2007 [doi>10.1109/ICAC.2007.35]
• 13: A. Naveh et al. Power and thermal management in the intel core duo processor. Intel Technology J., 10(2), May 2006.
• 14: U. E. P. A. E. S. Program. Report to congress on server and data center energy efficiency. http://www.energystar.gov/ia/partners/prod_development/downloads/EPA_ Datacenter_Report_Congress_Finall.pdf, 2007.
• 15: Omer F. Rana , Jeffrey O. Kephart, Building Effective Multivendor Autonomic Computing Systems, IEEE Distributed Systems Online, v.7 n.9, p.3, September 2006 [doi>10.1109/MDSO.2006.53]
• 16: Gerald Tesauro , David M. Chess , William E. Walsh , Rajarshi Das , Alla Segal , Ian Whalley , Jeffrey O. Kephart , Steve R. White, A Multi-Agent Systems Approach to Autonomic Computing, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, p.464-471, July 19-23, 2004, New York, New York [doi>10.1109/AAMAS.2004.23]
• 17: G. Tesauro et al. Managing power consumption and performance of computing systems using reinforcement learning. In Proc. of NIPS-07, 2007.
• 18: G. Tesauro, N. K. Jong, R. Das, and M. N. Bennani. A hybrid reinforcement learning approach to autonomic resource allocation. In Proc. of ICAC-06, pages 65--73, 2006.
• 19: The Apache Software Foundation. Apache HTTP server version 2.2. http://httpd.apache.org/docs/2.2/, 2007.
• 20: The Green Grid Consortium. The green grid. http://www.thegreengrid.org/, 2007.

INDEX TERMS

Primary Classification:
  D. Software
  D.4 OPERATING SYSTEMS
      D.4.8 Performance

Keywords:
data center, energy savings, green data center, multicriteria utility functions, policy-based management, power management, power measurement