Abstract
Nowadays, virtualization solutions are gaining increasing importance. By enabling the sharing of physical resources, thus making resource usage more efficient, they promise energy and cost savings. Additionally, virtualization is the key enabling technology for cloud computing and server consolidation. However, resource sharing and other factors have direct effects on system performance, which are not yet well-understood. Hence, performance prediction and performance management of services deployed in virtualized environments like public and private clouds is a challenging task. Because of the large variety of virtualization solutions, a generic approach to predict the performance overhead of services running on virtualization platforms is highly desirable. In this paper, we present a methodology to quantify the influence of the identified performance-relevant factors based on an empirical approach using benchmarks. We show experimental results on two popular state-of-the-art virtualization platforms, Citrix XenServer 5.5 and VMware ESX 4.0, as representatives of the two major hypervisor architectures. Based on these results, we propose a basic, generic performance prediction model for the two different types of hypervisor architectures. The target is to predict the performance overhead for executing services on virtualized platforms.
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Notes
- 1.
Passmark PerformanceTest: http://www.passmark.com/products/pt.htm.
- 2.
SPEC CPU2006: http://www.spec.org/cpu2006/.
- 3.
Iperf: http://iperf.sourceforge.net/.
References
P. Apparao, R. Iyer, X. Zhang, D. Newell, and T. Adelmeyer. Characterization & Analysis of a Server Consolidation Benchmark. In VEE ’08, 2008.
P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield. Xen and the Art of Virtualization. In SOSP, 2003.
K. Czarnecki and U. W. Eisenecker. Generative Programming. Addison-Wesley Longman, Amsterdam, 2000.
Descartes Research Group. http://www.descartes-research.net, July 2011.
N. Huber, M. von Quast, F. Brosig, and S. Kounev. Analysis of the Performance-Influencing Factors of Virtualization Platforms. In The 12th International Symposium on Distributed Objects, Middleware, and Applications (DOA), 2010.
N. Huber, M. von Quast, M. Hauck, and S. Kounev. Evaluating and Modeling Virtualization Performance Overhead for Cloud Environments. In International Conference on Cloud Computing and Service Science (CLOSER), 2011.
IDC. Virtualization Market Accelerates Out of the Recession as Users Adopt “Virtualize First” Mentality. http://www.idc.com/getdoc.jsp?containerId=prUS22316610, April 2010.
IT world, The IDG Network. Gartner’s data on energy consumption, virtualization, cloud. http://www.itworld.com/green-it/59328/gartners-data-energy-consumption-virtualization-cloud, 2008.
R. Iyer, R. Illikkal, O. Tickoo, L. Zhao, P. Apparao, and D. Newell. VM3: Measuring, modeling and managing VM shared resources. Computer Networks, 53(17):2873–2887, 2009.
Y. Koh, R. C. Knauerhase, P. Brett, M. Bowman, Z. Wen, and C. Pu. An analysis of performance interference effects in virtual environments. In ISPASS, 2007.
S. Kounev, F. Brosig, N. Huber, and R. Reussner. Towards self-aware performance and resource management in modern service-oriented systems. In SCC’10.
D. A. Menascé, V. A. F. Almeida, and L. W. Dowdy. Capacity Planning and Performance Modeling – From Mainframes to Client-Server Systems. Prentice-Hall, Upper Saddle River, New Jersey, USA, 1994.
P. Padala, X. Zhu, Z. Wang, S. Singhal, and K. G. Shin. Performance evaluation of virtualization technologies for server consolidation. HP Labs Tec. Report, 2007.
B. Quétier, V. Néri, and F. Cappello. Scalability Comparison of Four Host Virtualization Tools. Jounal on Grid Computing, 5(1):83–98, 2007.
M. Rosenblum and T. Garfinkel. Virtual machine monitors: current technology and future trends. Computer, 38(5):39–47, 2005.
M. Salsburg. Beyond the Hypervisor Hype. In CMG Conference, 2007.
S. Soltesz, H. Pötzl, M. E. Fiuczynski, A. Bavier, and L. Peterson. Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors. SIGOPS Oper. Syst. Rev., 41(3):275–287, 2007.
O. Tickoo, R. Iyer, R. Illikkal, and D. Newell. Modeling virtual machine performance: Challenges and approaches. In HotMetrics, 2009.
VMware. A performance comparison of hypervisors. http://www.vmware.com/pdf/hypervisor_performance.pdf, 2007.
D. Westermann, J. Happe, M. Hauck, and C. Heupel. The Performance Cockpit Approach: A Framework for Systematic Performance Evaluations. In SEAA’10.
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This work was funded by the German Research Foundation (DFG) under grant No. 3445/6-1.
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Huber, N., von Quast, M., Brosig, F., Hauck, M., Kounev, S. (2012). A Method for Experimental Analysis and Modeling of Virtualization Performance Overhead. In: Ivanov, I., van Sinderen, M., Shishkov, B. (eds) Cloud Computing and Services Science. CLOSER 2011. Service Science: Research and Innovations in the Service Economy. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-2326-3_19
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