Intel Virtualization Technology
Computer
Power reduction techniques for microprocessor systems
ACM Computing Surveys (CSUR)
JouleSort: a balanced energy-efficiency benchmark
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Dynamic Voltage Scaling in Multitier Web Servers with End-to-End Delay Control
IEEE Transactions on Computers
pMapper: power and migration cost aware application placement in virtualized systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Entropy: a consolidation manager for clusters
Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Shares and utilities based power consolidation in virtualized server environments
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Energy aware consolidation for cloud computing
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Energy efficient utilization of resources in cloud computing systems
The Journal of Supercomputing
Batch scheduling of consolidated virtual machines based on their workload interference model
Future Generation Computer Systems
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Rapid growth of large-scale applications and their widespread use in research and industry has led to dramatic increases in energy consumption in enterprise data centers and large-scale distributed systems such as Grids. Any attempt at reducing the energy consumption without concern for performance can be destructive and deteriorate the overall efficiency of data centers and large-scale distributed systems running such applications. In this paper, we present an optimization model for resource management in virtualized distributed systems to minimize power costs automatically while satisfying performance constraints. The objective of our model is to keep the utilization of servers near to an optimum point to prevent performance degradation. The model includes two objective functions, one for power costs and another for performance. Using the objective functions, we present a scheduling algorithm to place a set of virtual machines on a set of servers dynamically so that to integrate power management with performance management. We show experimentally that the proposed scheduler consumes approximately 24% less energy than static power management techniques while maintaining comparable performance.