Adaptive Control
Power provisioning for a warehouse-sized computer
Proceedings of the 34th annual international symposium on Computer architecture
Coordinating Multiple Autonomic Managers to Achieve Specified Power-Performance Tradeoffs
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Proceedings of the VLDB Endowment
Autonomic Power and Performance Management for Computing Systems
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems
Middleware '08 Proceedings of the ACM/IFIP/USENIX 9th International Middleware Conference
Entropy: a consolidation manager for clusters
Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Automated control of multiple virtualized resources
Proceedings of the 4th ACM European conference on Computer systems
vManage: loosely coupled platform and virtualization management in data centers
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Stochastic approximation control of power and tardiness in a three-tier web-hosting cluster
Proceedings of the 7th international conference on Autonomic computing
Energy-Aware Scheduling in Virtualized Datacenters
CLUSTER '10 Proceedings of the 2010 IEEE International Conference on Cluster Computing
ICPPW '10 Proceedings of the 2010 39th International Conference on Parallel Processing Workshops
Virtualized Web server cluster self-configuration to optimize resource and power use
Journal of Systems and Software
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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Nowadays, one of the most important goals of data center management is to maximize their profit by minimizing power consumption and service-level agreement (SLA) violations of hosted applications. System dynamics make it difficult to implement optimization in both aspects on shared infrastructures. A majority of existing works either focused on one aspect or applied models that are trained offline for application-specific workload. In addition, virtualization is being widely used in large-scale data centers to attain basic benefits like fault and performance isolation, and to improve system manageability. A key challenge that comes with virtualization is to dynamically provisioning resources for virtual machines and optimize their capacity for meeting service level objectives at the lowest possible cost. In this paper, we present a hierarchical management framework to assure application-level performance while minimizing power consumption for virtualized data centers. A novelty of the management framework is to combine control theory with linear programming technique. Empirical results show that the proposed framework brings substantial energy saving, while ensuring application performance. Especially, the integration of the performance controller and the energy optimizer results in an energy saving of 43% on our hardware testbed.