DENS: Data Center Energy-Efficient Network-Aware Scheduling
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Snooze: A Scalable, Fault-Tolerant and Distributed Consolidation Manager for Large-Scale Clusters
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Towards a green cluster through dynamic remapping of virtual machines
Future Generation Computer Systems
Energy-Aware Ant Colony Based Workload Placement in Clouds
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
WSEAS Transactions on Information Science and Applications
DENS: data center energy-efficient network-aware scheduling
Cluster Computing
A survey of migration mechanisms of virtual machines
ACM Computing Surveys (CSUR)
A multi-objective ant colony system algorithm for virtual machine placement in cloud computing
Journal of Computer and System Sciences
Survey of Energy Efficient Data Centers in Cloud Computing
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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With the increasing prevalence of large scale cloud computing environments, how to place requested applications into available computing servers regarding to energy consumption has become an essential research problem, but existing application placement approaches are still not effective for live applications with dynamic characters. In this paper, we proposed a novel approach named EnaCloud, which enables application live placement dynamically with consideration of energy efficiency in a cloud platform. In EnaCloud, we use a Virtual Machine to encapsulate the application, which supports applications scheduling and live migration to minimize the number of running machines, so as to save energy. Specially, the application placement is abstracted as a bin packing problem, and an energy-aware heuristic algorithm is proposed to get an appropriate solution. In addition, an over-provision approach is presented to deal with the varying resource demands of applications. Our approach has been successfully implemented as useful components and fundamental services in the iVIC platform. Finally, we evaluate our approach by comprehensive experiments based on virtual machine monitor Xen and the results show that it is feasible.