Energy Aware Virtual Machine Migration Techniques for Cloud Environment

Cloud Computing offers indispensable infrastructure for storage and computing facilities for development of diversified services. The large utilization of resources leads to increased energy consumption that has imposed a limit on performance growth. Owing to high operational costs and carbon dioxide footprints, an efficient energy management technique needs to be developed and deployed that reduces overall energy consumption of a cloud environment while maximizing the resource utilization. In the first phase of this research, some virtual machine migration techniques were explored. In the second phase, a virtual machine migration technique has been implemented which aims at reducing energy consumption in cloud datacentres. General Terms Virtual Machine Migration, Bin Packing Algorithms.

[1]  Rajkumar Buyya,et al.  Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges , 2010, PDPTA.

[2]  Qiang Zhang,et al.  The Characteristics of Cloud Computing , 2010, 2010 39th International Conference on Parallel Processing Workshops.

[3]  Geoffrey C. Fox,et al.  Analysis of Virtualization Technologies for High Performance Computing Environments , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[4]  Roberto Di Pietro,et al.  Secure virtualization for cloud computing , 2011, J. Netw. Comput. Appl..

[5]  Yi Zhong,et al.  State-of-the-art research study for green cloud computing , 2011, The Journal of Supercomputing.

[6]  Christine Morin,et al.  Experimental Study on the Energy Consumption in IaaS Cloud Environments , 2013, 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing.

[7]  Raihan Ur Rasool,et al.  Software level green computing for large scale systems , 2011, Journal of Cloud Computing: Advances, Systems and Applications.

[8]  Filip De Turck,et al.  Efficient resource management for virtual desktop cloud computing , 2012, The Journal of Supercomputing.

[9]  Yamuna Nagar,et al.  A Review on Energy Efficient Techniques in Green Cloud , 2015 .

[10]  A. Volokyta,et al.  Secure virtualization in cloud computing , 2012, Proceedings of International Conference on Modern Problem of Radio Engineering, Telecommunications and Computer Science.

[11]  Arunima Jaiswal,et al.  Virtualization in Cloud Computing , 2014 .

[12]  Pinal Salot,et al.  A SURVEY OF VARIOUS SCHEDULING ALGORITHM IN CLOUD COMPUTING ENVIRONMENT , 2013 .

[13]  Thanasis Loukopoulos,et al.  Application-Aware Workload Consolidation to Minimize Both Energy Consumption and Network Load in Cloud Environments , 2013, 2013 42nd International Conference on Parallel Processing.

[14]  Yin Yang,et al.  ABACUS: An Auction-Based Approach to Cloud Service Differentiation , 2013, 2013 IEEE International Conference on Cloud Engineering (IC2E).

[15]  S MarySairaBhanu,et al.  DYNAMIC ENERGY MANAGEMENT IN CLOUD DATA CENTERS: A SURVEY , 2013, CloudCom 2013.

[16]  Rami G. Melhem,et al.  Power-aware scheduling for periodic real-time tasks , 2004, IEEE Transactions on Computers.

[17]  Keqiu Li,et al.  Energy Consumption in Cloud Computing Data Centers , 2014, CloudCom 2014.

[18]  Frank Leymann,et al.  Combining Different Multi-tenancy Patterns in Service-Oriented Applications , 2009, 2009 IEEE International Enterprise Distributed Object Computing Conference.

[19]  M. Durairaj A Study On Virtualization Techniques And Challenges In Cloud Computing , 2014 .

[21]  Subhajyoti Bandyopadhyay,et al.  Cloud computing - The business perspective , 2011, Decis. Support Syst..

[22]  Arindam Banerjee,et al.  Energy Efficiency Model for Cloud Computing , 2013 .