Analytical performance prediction on multicomputers
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
VirtualPower: coordinated power management in virtualized enterprise systems
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
pMapper: power and migration cost aware application placement in virtualized systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Optimal power allocation in server farms
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Adaptive Workload Prediction of Grid Performance in Confidence Windows
IEEE Transactions on Parallel and Distributed Systems
Robust Optimization Made Easy with ROME
Operations Research
A Performance Study on the VM Startup Time in the Cloud
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
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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.