ABSTRACT
Energy management has become a significant concern in data centers to reduce operational costs and maintain systems' reliability. Using virtualization allows server consolidation, which increases server utilization and reduces energy consumption by turning off unused servers. However, server consolidation and turning off servers can cause also consequences if they are not exploited efficiently. For instance, many researchers consider a deterministic demand for capacity planning, but the demand is always subject to uncertainty. This uncertainty is an outcome of the workload prediction and the workload fluctuation. This paper presents a robust optimization for proactive capacity planning. We do not presume that the demand of VMs is deterministic. Thus, we implement a range prediction approach instead of a single point prediction. Then, we implement a robust optimization model exploiting the range-based prediction to determine the number of active servers for each capacity planning period. The results of the simulation show that our approach can mitigate undesirable changes in the power-state of the servers. Additionally, the results indicate an increase in the servers' availability for hosting new VMs and reliability against a system failure during power-state changes. As future work, we intend to apply our approach to dynamic workload such as a web application. We plan to investigate applying our approach to other resources, where we consider only the CPU demand of VMs. Finally, we compare our approach against the approaches using stochastic optimization.
- R. Nathuji, K. Schwan, "Virtualpower: Coordinated power management in virtualized enterprise systems," ACM SIGOPS Operating Systems Review, 41(6) pp. 265--278, 2007 Google ScholarDigital Library
- A. Verma, P. Ahuja, A. Neogi, "PMAPPER: Power and Migration Cost Aware Application Placement in Virtualized Systems," proceedings of the 9th ACM/IFIP/USENIX Middleware,pp. 243--264,Belgium. Google ScholarDigital Library
- A. Gandhi, M. Harchol-balter, R. Das, C. Lefurgy, "Optimal Power Allocation in Server Farms," proceedings of the 11th International Joint Conference on MMCS, ACM New York, USA, 2009; 157--168. Google ScholarDigital Library
- Booting-time.{online}.available:http://www.tomshardware.co.uk/ubuntu-oneiric-ocelot-benchmark-review,review-32377-15.html.{accessed: 1-jun-2012}.Google Scholar
- M. Mao and M. Humphrey, "A Performance Study on the VM Startup Time in the Cloud," proc. of fifth IEEE Conference on Cloud Computing, 2012. Google ScholarDigital Library
- Planet-lab. available:http://www.planet-lab.org/Google Scholar
- A. Ben-Tal and A. Nemirovski, "Robust Solutions of Linear Programming Problems Contaminated with Uncertain Data," mathematical programming v. 88 (2000), 411--424.Google Scholar
- C. Dance and A. Gaivoronski, "Stochastic Optimization for Real Time Service Capacity Allocation under Random Service Demand," annals of operations research, vol. 193, no. 1, pp. 221--253, feb. 2011.Google Scholar
- W. Yanbo, "Empirical Comparison of Robust, Data Driven and Stochastic Optimization," MIT 2008.Google Scholar
- Y. Wu, K. Hwang, Y. Yuan , and W. Zheng, "Adaptive Workload Prediction of Grid Performance in Confidence Windows," IEEE trans. on PDS 2009 Google ScholarDigital Library
- M. J. Clement and M.J. Quinn, "Analytical Performance Prediction on Multicomputers," J. Supercomputing, pp. 886--894, 1993. Google ScholarDigital Library
- K. D. Lange, "Identifying Shades of Green: The SPECpower Benchmarks," IEEE Computer 42(3), pp. 95--97, 2009. Google ScholarDigital Library
- SPEC-results.{online}.available. http://www.spec.org/power_ssj2008/results/res2011q1/powe$r_ssj2008-20110127-00342.htmlGoogle Scholar
- J. Goh and M. Sim, "Robust Optimization Made Easy with Rome," Operations Research, 2011, pp.973--985. Google ScholarDigital Library
Index Terms
- A robust optimization for proactive energy management in virtualized data centers
Recommendations
Multiperiod robust optimization for proactive resource provisioning in virtualized data centers
Energy management has become a significant concern in data centers for reducing operational costs. Using virtualization allows server consolidation, which increases server utilization and reduces energy consumption by turning off idle servers. This ...
Robust Virtual Machine Consolidation for Efficient Energy and Performance in Virtualized Data Centers
ITHINGS '14: Proceedings of the 2014 IEEE International Conference on Internet of Things(iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom)Cloud providers use virtualization technologies to provide an isolated execution environment and agile resource provisioning. However, virtualized data centers consume huge amounts of energy, which increases the operational costs. To optimize resource ...
Server consolidation with migration control for virtualized data centers
Virtualization has become a key technology for simplifying service management and reducing energy costs in data centers. One of the challenges faced by data centers is to decide when, how, and which virtual machines (VMs) have to be consolidated into a ...
Comments