Snooze: A Scalable, Fault-Tolerant and Distributed Consolidation Manager for Large-Scale Clusters
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Performance and energy modeling for live migration of virtual machines
Proceedings of the 20th international symposium on High performance distributed computing
A multi-objective approach to virtual machine management in datacenters
Proceedings of the 8th ACM international conference on Autonomic computing
Energy-Aware Ant Colony Based Workload Placement in Clouds
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
Multi-objective virtual machine selection for migrating in virtualized data centers
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Performance and energy modeling for live migration of virtual machines
Cluster Computing
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Modern data centers usually have computing resources sized to handle expected peak demand, but average demand is generally much lower than peak. This means that the systems in the data center usually operate at very low utilization rates. Past techniques have exploited this fact to achieve significant power savings, but they generally focus on centrally managed, throughput-oriented systems that process a single fine-grained request stream. We propose a more general solution — a technique to save power by dynamically migrating virtual machines and packing them onto fewer physical machines when possible. We call our scheme Power-Aware Domain Distribution (PADD). In this paper, we report on simulation results for PADD and demonstrate that the power and performance changes from using PADD are primarily dependent on how much buffering or reserve capacity it maintains. Our adaptive buffering scheme achieves energy savings within 7% of the idealized system that has no performance penalty. Our results also show that we can achieve an energy savings up to 70% with fewer than 1% of the requests violating their service level agreements.