Energy management for hypervisor-based virtual machines
ATC'07 2007 USENIX Annual Technical Conference on Proceedings of the USENIX Annual Technical Conference
Vpm tokens: virtual machine-aware power budgeting in datacenters
HPDC '08 Proceedings of the 17th international symposium on High performance distributed computing
Power and Performance Management of Virtualized Computing Environments Via Lookahead Control
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
vManage: loosely coupled platform and virtualization management in data centers
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Q-clouds: managing performance interference effects for QoS-aware clouds
Proceedings of the 5th European conference on Computer systems
Virtual machine power metering and provisioning
Proceedings of the 1st ACM symposium on Cloud computing
WattApp: an application aware power meter for shared data centers
Proceedings of the 7th international conference on Autonomic computing
CoolIT: coordinating facility and it management for efficient datacenters
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Enhancing data center sustainability through energy-adaptive computing
ACM Journal on Emerging Technologies in Computing Systems (JETC)
Evaluating the need for complexity in energy-aware management for cloud platforms
ACM SIGMETRICS Performance Evaluation Review
Virtual machine power measuring technique with bounded error in cloud environments
Journal of Network and Computer Applications
Performance and energy modeling for live migration of virtual machines
Cluster Computing
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This paper explores the feasibility of and challenges in developing methods for black-box monitoring of the power usage of a virtual machine (VM) at run-time, on shared virtualized compute platforms, including those with complex memory hierarchies. We demonstrate that VM-level power utilization can be accurately estimated, or estimated with accuracy with bound error margins. The use of bounds permits more lightweight online monitoring of fewer events, while relaxing the fidelity of the estimates in a controlled manner. Our methodology is evaluated on the Intel Core i7 and Core2 x86-64 platforms, running synthetic and SPEC benchmarks.