Reducing electricity cost through virtual machine placement in high performance computing clouds
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
A Power and Performance Management Framework for Virtualized Server Clusters
GREENCOM '11 Proceedings of the 2011 IEEE/ACM International Conference on Green Computing and Communications
Adaptive Scheduling on Power-Aware Managed Data-Centers Using Machine Learning
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
Energy-efficient and multifaceted resource management for profit-driven virtualized data centers
Future Generation Computer Systems
A request multiplexing method based on multiple tenants in saas
GPC'12 Proceedings of the 7th international conference on Advances in Grid and Pervasive Computing
A genetic algorithm for power-aware virtual machine allocation in private cloud
ICT-EurAsia'13 Proceedings of the 2013 international conference on Information and Communication Technology
Empowering automatic data-center management with machine learning
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Business-driven management of infrastructure-level risks in Cloud providers
Future Generation Computer Systems
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The reduction of energy consumption in large-scale datacenters is being accomplished through an extensive use of virtualization, which enables the consolidation of multiple workloads in a smaller number of machines. Nevertheless, virtualization also incurs some additional overheads (e.g. virtual machine creation and migration) that can influence what is the best consolidated configuration, and thus, they must be taken into account. In this paper, we present a dynamic job scheduling policy for power-aware resource allocation in a virtualized datacenter. Our policy tries to consolidate workloads from separate machines into a smaller number of nodes, while fulfilling the amount of hardware resources needed to preserve the quality of service of each job. This allows turning off the spare servers, thus reducing the overall datacenter power consumption. As a novelty, this policy incorporates all the virtualization overheads in the decision process. In addition, our policy is prepared to consider other important parameters for a datacenter, such as reliability or dynamic SLA enforcement, in a synergistic way with power consumption. The introduced policy is evaluated comparing it against common policies in a simulated environment that accurately models HPC jobs execution in a virtualized datacenter including power consumption modeling and obtains a power consumption reduction of 15% with respect to typical policies.