Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
VirtualPower: coordinated power management in virtualized enterprise systems
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Speed scaling with an arbitrary power function
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
pMapper: power and migration cost aware application placement in virtualized systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Optimal power allocation in server farms
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Energy Efficient Resource Management in Virtualized Cloud Data Centers
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Energy aware consolidation for cloud computing
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
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Power management is one of the most challenging problems in cloud computing. A cloud data center could save the amount of energy used from speed scaling. The traditional theoretical research for speed scaling usually assume the power function as the form Sα. Moreover, more comprehensive support for Quality of Service (QoS) is essential by cloud computing providers. Thus, how to dealing with the power/performance trade-off is a burning question. Motivated by improving energy efficiency of the data center, we study policies by setting the speed of the processor for both goals of minimizing the total energy cost and meeting the specified QoS performance well. We initiate a model of speed scaling with weighted power energy, the QoS parameters can be induced to a qualitative concept as the weighting factor of energy consumptions. Based on this model, we propose a resource allocation policy based on the cooperative game theory for energy-efficient management of clouds. The simulation results show the efficiency of the method.