Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Convex Optimization
IEEE Transactions on Parallel and Distributed Systems
Power provisioning for a warehouse-sized computer
Proceedings of the 34th annual international symposium on Computer architecture
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
No "power" struggles: coordinated multi-level power management for the data center
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems
Energy-aware server provisioning and load dispatching for connection-intensive internet services
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
Power and Performance Management of Virtualized Computing Environments Via Lookahead Control
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
pMapper: power and migration cost aware application placement in virtualized systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Cutting the electric bill for internet-scale systems
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Thermal aware server provisioning and workload distribution for internet data centers
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Server workload analysis for power minimization using consolidation
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
Modeling and synthesizing task placement constraints in Google compute clusters
Proceedings of the 2nd ACM Symposium on Cloud Computing
CloudScale: elastic resource scaling for multi-tenant cloud systems
Proceedings of the 2nd ACM Symposium on Cloud Computing
Heterogeneity and dynamicity of clouds at scale: Google trace analysis
Proceedings of the Third ACM Symposium on Cloud Computing
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Data centers have recently gained significant popularity as a cost-effective platform for hosting large-scale service applications. While large data centers enjoy economies of scale by amortizing initial capital investment over large number of machines, they also incur tremendous energy cost in terms of power distribution and cooling. An effective approach for saving energy in data centers is to adjust dynamically the data center capacity by turning off unused machines. However, this dynamic capacity provisioning problem is known to be challenging as it requires a careful understanding of the resource demand characteristics as well as considerations to various cost factors, including task scheduling delay, machine reconfiguration cost and electricity price fluctuation. In this paper, we provide a control-theoretic solution to the dynamic capacity provisioning problem that minimizes the total energy cost while meeting the performance objective in terms of task scheduling delay. Specifically, we model this problem as a constrained discrete-time optimal control problem, and use Model Predictive Control (MPC) to find the optimal control policy. Through extensive analysis and simulation using real workload traces from Google's compute clusters, we show that our proposed framework can achieve significant reduction in energy cost, while maintaining an acceptable average scheduling delay for individual tasks.