AppFlow-based Autonomic Performance/Watt Management of Large-scale Data Centers

1. References Conclusion Related Work Results and Evaluation Autonomic Power and Performance Management Problem Acknowledgements AppFlow-based Autonomic Performance/Watt Management of Large-scale Data Centers Bithika Khargharia1,Salim Hariri1, Manal Houri2 ,Hesham El-Rewini2 , Samee Khan3, Ishfaq Ahmad3 and Mazin S. Yousif4 1 Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721 2Southern Methodist University, Dallas, TX,75275 3University of Texas at Arlington, Arlington, TX, 76019 4 Corporate Technology Group (CTG), Intel Corporation, Hillsboro, OR 97123 NSF Center for Autonomic Computing http://www.ece.arizona.edu/~hpdc http://www.nsfcac.org • 63% of the TCO of data center’s physical infrastructure is related to power consumption. • Data centers consumed about 61 billion kilowatt-hours (kWh) in 2006 for a total electricity cost of about $4.5 billion [1] • Energy use in 2006 is estimated to be more than double the electricity that was consumed for this purpose in 2000 depicting an upward trend in energy consumption [1]. The Environmental Problem The Financial Problem The Technical Problem Fig. 2: Autonomic Data Center Fig. 1: The AppFlow Data Structure Fig. 4: SPECjbb 2005 Heap Usage Fig. 5: Temporal Variation of SPECjbb2005 working set pages (memory ranks) Features • Instantaneous Optimization for power and performance at each level of the data center hierarchy. • AppFlow characterizes dynamic (spatial and temporal) behavior and resource requirements of data-center workloads. • AppFlow is coherent and consistent across all hierarchies of data- center managed systems. Fig. 7: Optimal and sub-optimal memory configuration Fig. 6: SPECjbb2005 Performance-per-Watt Analysis Fig. 3:AppFlow for Server (platform) used to establish the instantaneous system operating point and its trajectory. The most power and performance efficient server configuration is determined based on the trajectory of the operating point. If the operating point moves into “anomalous” operating zone it is moved back into safe-zone by reconfiguring the server which involves reconfiguring the CPU and Memory. Fig. 8: Power Savings (K=1.0) Fig. 9: Make-span Comparison (K=1.0) 48.8 % (26.7 kJ) power saving for interleaved memory 65% power savings for server cluster This work was supported in part by: Grant from NSF/NGS Contract 0305427 and NSF/SEI(EAR) Contract 0431079. • Processors - Frequency-scaling, clock-throttling, Dynamic Voltage Scaling (DVS), Memory – multiple • power states, Disks –multiple speeds • DVS and Cluster Turn On/Off [2]. • Power-aware QoS Mgmt [3]. • Load-balancing/Un-balancing [4]. • Theoretical and experimental framework to optimize power and performance at runtime for e-business data centers. • Data structure to support runtime decision-making for power & performance management. • Case Study: Cluster, Server and Interleaved Memory Power Management in a data center. 1] EPA Report to Congress on Server and Data Center Energy Efficiency, March 2007. 2] Elnozahy et al., Energy-Efficient Server Clusters. PACS, Feb. 2002. 3] Pinheiro et al., “Load Balancing and Unbalancing for Power and Performance in Cluster-Based Systems,” COSLP, Sept. 2001 4] Sharma et al., Power-aware QoS Management in Web Servers, RTSS, Dec., 2003.