Run-time modeling and estimation of operating system power consumption
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Power prediction for intel XScale® processors using performance monitoring unit events
ISLPED '05 Proceedings of the 2005 international symposium on Low power electronics and design
Power provisioning for a warehouse-sized computer
Proceedings of the 34th annual international symposium on Computer architecture
Virtual machine power metering and provisioning
Proceedings of the 1st ACM symposium on Cloud computing
QoS Aware Semantic Web Service Composition Approach Considering Pre/Postconditions
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
A comparison of high-level full-system power models
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Service-Oriented Computing and Cloud Computing: Challenges and Opportunities
IEEE Internet Computing
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With the emergence of commodity computing environments (i.e. clouds), information technology (IT) infrastructure providers are creating data centers in distributed geographical regions. Since geographic regions have different costs and demands on their local power grids, cloud computing infrastructures will require innovative management procedures to ensure energy-efficiency that spans multiple regions. Macro-level measurement of energy consumption that focuses on the individual servers does not have the dynamism to respond to situations where domain-specific software services are migrated to different data centers in varying regions. Next-generation models will have to understand the impact on power consumption for a particular software application or software service, at a micro-level. A challenge to this approach is to develop a prediction of energy conservation a priori. In this work, we discuss the challenges for measuring the power consumption of an individual web service. We discuss the challenges of determining the power consumption profile of a web service each time it is migrated to a new server and the training procedure of the power model. This potentially promotes creating a dynamically-green cloud infrastructure.