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An Energy-Efficient VM Prediction and Migration Framework for Overcommitted Clouds - 2018

Research Area:  Cloud Computing


We propose an integrated, energy-efficient, resource allocation framework for overcommitted clouds. The framework makes great energy savings by 1) minimizing Physical Machine (PM) overload occurrences via VM resource usage monitoring and prediction, and 2) reducing the number of active PMs via efficient VM migration and placement. Using real Google data consisting of a 29-day traces collected from a cluster containing more than 12K PMs, we show that our proposed framework outperforms existing overload avoidance techniques and prior VM migration strategies by reducing the number of unpredicted overloads, minimizing migration overhead, increasing resource utilization, and reducing cloud energy consumption.

Author(s) Name:  Mehiar Dabbagh,Bechir Hamdaoui,Mohsen Guizani and Ammar Rayes

Journal name:  IEEE Transactions on Cloud Computing

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TCC.2016.2564403

Volume Information:  Oct.-Dec. 2018, pp. 955-966, vol. 6