Managing server energy and operational costs in hosting centers
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer 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
A dynamic optimization model for power and performance management of virtualized clusters
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications
IEEE Transactions on Parallel and Distributed Systems
Server workload analysis for power minimization using consolidation
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
Power-Aware Consolidation of Scientific Workflows in Virtualized Environments
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science
Energy Conscious Scheduling for Distributed Computing Systems under Different Operating Conditions
IEEE Transactions on Parallel and Distributed Systems
Enabling consolidation and scaling down to provide power management for cloud computing
HotCloud'11 Proceedings of the 3rd USENIX conference on Hot topics in cloud computing
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How to achieve energy efficiency to run a cloud data center is a major challenge in the era of rising electricity cost and environmental protection. Various techniques have been devised to help reduce energy consumption for cloud data centers that consist of a large number of identical servers, including dynamic allocation of active servers, consolidating diverse applications to run on them, and adjusting the CPU speed of an active server. Leveraging these techniques, we use an Online Coloring Bin Packing problem to model the consolidation problem and devise an effective application-aware approximation algorithm to find a near-optimal solution. We show a 1.7 asymptotic approximation ratio. We then apply a Predictive Bayesian Network model to identify daily workload patterns and adjust resource provisioning accordingly. We evaluate our approaches using traces collected from a real data center and demonstrate that (1) our prediction algorithm is effective in estimating future demands, (2) our coordinated approaches can provide significant savings of energy and operational costs close to the near-optimal offline solution, and (3) our approaches incur little reliability costs in term of wear-and-tear of server components.