ABSTRACT
Virtualization is often used in cloud computing platforms for its several advantages in efficiently managing resources. However, virtualization raises certain additional challenges, and one of them is lack of power metering for virtual machines (VMs). Power management requirements in modern data centers have led to most new servers providing power usage measurement in hardware and alternate solutions exist for older servers using circuit and outlet level measurements. However, VM power cannot be measured purely in hardware. We present a solution for VM power metering, named Joulemeter. We build power models to infer power consumption from resource usage at runtime and identify the challenges that arise when applying such models for VM power metering. We show how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error. Our approach is designed to operate with extremely low runtime overhead while providing practically useful accuracy. We illustrate the use of the proposed metering capability for VM power capping, a technique to reduce power provisioning costs in data centers. Experiments are performed on server traces from several thousand production servers, hosting Microsoft's real-world applications such as Windows Live Messenger. The results show that not only does VM power metering allows virtualized data centers to achieve the same savings that non-virtualized data centers achieved through physical server power capping, but also that it enables further savings in provisioning costs with virtualization.
- Y. Bao, M. Chen, Y. Ruan, L. Liu, J. Fan, Q. Yuan, B. Song, and J. Xu. HMTT: A platform independent full-system memory trace monitoring system. In ACM Sigmetrics, June 2008. Google ScholarDigital Library
- F. Bellosa. The benefits of event-driven energy accounting in power-sensitive systems. In In Proceedings of the 9th ACM SIGOPS European Workshop, 2000. Google ScholarDigital Library
- W. L. Bircher and L. K. John. Complete system power estimation: A trickle-down approach based on performance events. In International Symposium on Performance Analysis Systems and Software (ISPASS), 2007.Google ScholarCross Ref
- D. Brooks, V. Tiwari, and M. Martonosi. Wattch: a framework for architectural-level power analysis and optimizations. In ISCA '00: Proceedings of the 27th annual international symposium on Computer architecture, pages 83--94, 2000. Google ScholarDigital Library
- D. Economou, S. Rivoire, C. Kozyrakis, and P. Ranganathan. Full-system power analysis and modeling for server environments. In Workshop on Modeling, Benchmarking and Simulation (MoBS), June 2006.Google Scholar
- 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. Google ScholarDigital Library
- M. Femal and V. Freeh. Safe overprovisioning: Using power limits to increase aggregate throughput. In Workshop on Power-Aware Computer Systems (PACS), Portland, OR, December 2004. Google ScholarDigital Library
- J. Flinn and M. Satyanarayanan. Powerscope: A tool for profiling the energy usage of mobile applications. In WMCSA '99: Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications, 1999. Google ScholarDigital Library
- R. Fonseca, P. Dutta, P. Levis, and I. Stoica. Quanto: Tracking energy in networked embedded systems. In Symposium on Operating System Design and Implementation (OSDI), December 2008. Google ScholarDigital Library
- J. Hamilton. Cost of power in large-scale data centers. Blog entry dated 11/28/2008 at http://perspectives.mvdirona.com. Also in Keynote, at ACM SIGMETRICS 2009.Google Scholar
- HP. Dynamic power capping TCO and best practices white paper. http://h71028.www7.hp.com/ERC/downloads/4AA2-3107ENW.pdf.Google Scholar
- IBM. IBM active energy manager. http://www-03.ibm.com/systems/ management/director/about /director52/extensions/actengmrg.html.Google Scholar
- C. Im and S. Ha. Energy optimization for latency- and quality-constrained video applications. IEEE Des. Test, 21(5):358--366, 2004. Google ScholarDigital Library
- C. Isci and M. Martonosi. Runtime power monitoring in high-end processors: Methodology and empirical data. In 36th annual International Symposium on Microarchitecture (MICRO), 2003. Google ScholarDigital Library
- J. Janzen. Calculating memory system power for ddr sdram. Micro Designline, 10(2), 2001.Google Scholar
- J. Jenne, V. Nijhawan, and R. Hormuth. Dell energy smart architecture (desa) for 11g rack and tower servers. http://www.dell.com.Google Scholar
- Y. Kim, S. Gurumurthi, and A. Sivasubramaniam. Understanding the performancetemperature interactions in disk i/o of server workloads. In The Symposium on High-Performance Computer Architecture, pages 176--186, February 2006.Google Scholar
- C. Lefurgy, X. Wang, and M. Ware. Server-level power control. In Fourth International Conference on Autonomic Computing (ICAC), page 4, 2007. Google ScholarDigital Library
- A. Mahesri and V. Vardhan. Power consumption breakdown on a modern laptop. In Power-Aware Computer Systems, 4th International Workshop (PACS), Portland, OR, USA, December 2004. Google ScholarDigital Library
- R. Nathuji, P. England, P. Sharma, and A. Singh. Feedback driven qos-aware power budgeting for virtualized servers. In Fourth International Workshop on Feedback Control Implementation and Design in Computing Systems and Networks (FeBID), April 2009.Google Scholar
- A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, and B. Maggs. Cutting the Electric Bill for Internet-Scale Systems. In ACM SIGCOMM, Barcelona, Spain, August 2009. Google ScholarDigital Library
- F. Rawson. Mempower: A simple memory power analysis tool set. Technical report, IBM Austin Research Laboratory, 2004.Google Scholar
- S. Rivoire, P. Ranganathan, and C. Kozyrakis. A comparison of high-level full-system power models. In HotPower'08: Workshop on Power Aware Computing and Systems, December 2008. Google ScholarDigital Library
- A. Sinha and A. P. Chandrakasan. Jouletrack: a web based tool for software energy profiling. In 38th Conference on Design Automation (DAC), pages 220--225, 2001. Google ScholarDigital Library
- D. C. Snowdon, E. L. Sueur, S. M. Petters, and G. Heiser. Koala: A platform for os-level power management. In Proceedings of the 4th EuroSys Conference, Nuremberg, Germany, April 2009. Google ScholarDigital Library
- P. Stanley-Marbell and M. Hsiao. Fast, flexible, cycle-accurate energy estimation. In Proceedings of the International Symposium on Low power Electronics and Design, pages 141--146, 2001. Google ScholarDigital Library
- T. Stathopoulos, D. McIntire, and W. J. Kaiser. The energy endoscope: Real-time detailed energy accounting for wireless sensor nodes. In 7th international conference on Information processing in sensor networks (IPSN), pages 383--394, 2008. Google ScholarDigital Library
- C. Stewart and K. Shen. Some joules are more precious than others: Managing renewable energy in the datacenter. In Workshop on Power Aware Computing and Systems (HotPower), at SOSP, October 2009.Google Scholar
- J. Stoess, C. Lang, and F. Bellosa. Energy management for hypervisor-based virtual machines. In USENIX Annual Technical Conference, pages 1--14, 2007. Google ScholarDigital Library
- V. Tiwari, S. Malik, A. Wolfe, and M. T.-C. Lee. Instruction level power analysis and optimization of software. In 9th International Conference on VLSI Design, page 326, 1996. Google ScholarDigital Library
- B. Urgaonkar, P. Shenoy, and T. Roscoe. Resource overbooking and application profiling in a shared internet hosting platform. ACM Trans. Internet Technol., 9(1):1--45, 2009. Google ScholarDigital Library
- J. Zedlewski, S. Sobti, N. Garg, F. Zheng, A. Krishnamurthy, and R. Wang. Modeling hard-disk power consumption. In 2nd USENIX Conference on File and Storage Technologies (FAST), 2003. Google ScholarDigital Library
- H. Zeng, C. S. Ellis, A. R. Lebeck, and A. Vahdat. Ecosystem: managing energy as a first class operating system resource. In ASPLOS-X: Proceedings of the 10th international conference on Architectural support for programming languages and operating systems, pages 123--132, 2002. Google ScholarDigital Library
Index Terms
- Virtual machine power metering and provisioning
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