Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Genetic and Evolutionary Computation Conference
Evaluating the effectiveness of model-based power characterization
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
Self-management of applications QoS for energy optimization in datacenters
Green Computing Middleware on Proceedings of the 2nd International Workshop
Self-management of cloud applications and infrastructure for energy optimization
ACM SIGOPS Operating Systems Review
The Journal of Supercomputing
Enhancing data center sustainability through energy-adaptive computing
ACM Journal on Emerging Technologies in Computing Systems (JETC)
E2DC'12 Proceedings of the First international conference on Energy Efficient Data Centers
Autonomic performance-per-watt management (APM) of cloud resources and services
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
ACRA: a unified admission control and resource allocation framework for virtualized environments
Proceedings of the 8th International Conference on Network and Service Management
Optimization power consumption model of reliability-aware GPU clusters
The Journal of Supercomputing
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Today's data centers face two critical challenges. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, server power consumption must be controlled in order to avoid failures caused by power capacity overload or system overheating due to increasing high server density. However, existing work controls power and application-level performance separately, and thus, cannot simultaneously provide explicit guarantees on both. In addition, as power and performance control strategies may come from different hardware/software vendors and coexist at different layers, it is more feasible to coordinate various strategies to achieve the desired control objectives than relying on a single centralized control strategy. This paper proposes Co-Con, a novel cluster-level control architecture that coordinates individual power and performance control loops for virtualized server clusters. To emulate the current practice in data centers, the power control loop changes hardware power states with no regard to the application-level performance. The performance control loop is then designed for each virtual machine to achieve the desired performance even when the system model varies significantly due to the impact of power control. Co-Con configures the two control loops rigorously, based on feedback control theory, for theoretically guaranteed control accuracy and system stability. Empirical results on a physical testbed demonstrate that Co-Con can simultaneously provide effective control on both application-level performance and underlying power consumption.