Power provisioning for a warehouse-sized computer
Proceedings of the 34th annual international symposium on Computer architecture
Grid'5000: A Large Scale And Highly Reconfigurable Experimental Grid Testbed
International Journal of High Performance Computing Applications
VirtualPower: coordinated power management in virtualized enterprise systems
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Energy management for hypervisor-based virtual machines
ATC'07 2007 USENIX Annual Technical Conference on Proceedings of the USENIX Annual Technical Conference
Storage modeling for power estimation
SYSTOR '09 Proceedings of SYSTOR 2009: The Israeli Experimental Systems Conference
Evaluating memory energy efficiency in parallel I/O workloads
CLUSTER '07 Proceedings of the 2007 IEEE International Conference on Cluster Computing
Elastic management of cluster-based services in the cloud
ACDC '09 Proceedings of the 1st workshop on Automated control for datacenters and clouds
Demystifying energy consumption in Grids and Clouds
GREENCOMP '10 Proceedings of the International Conference on Green Computing
Profiling Energy Consumption of VMs for Green Cloud Computing
DASC '11 Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing
Macropower: A coarse-grain power profiling framework for energy-efficient cloud computing
PCCC '11 Proceedings of the 30th IEEE International Performance Computing and Communications Conference
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Energy consumption has always been a major concern in the design and cost of data centers. The wide adoption of virtualization and cloud computing has added another layer of complexity to enabling an energy-efficient use of computing power in large-scale settings. Among the many aspects that influence the energy consumption of a cloud system, the hardware-component level is one of the most intensively studied. However, higher-level factors such as virtual machine properties, their placement policies or application workloads may play an essential role in defining the power consumption profile of a given cloud system. In this paper, we explore the energy consumption patterns of Infrastructure-as-a-Service cloud environments under various synthetic and real application workloads. For each scenario, we investigate the power overhead triggered by different types of virtual machines, the impact of the virtual cluster size on the energy-efficiency of the hosting infrastructure and the tradeoff between performance and energy consumption of MapReduce virtual clusters through typical cloud applications.