Study of Energy Efficient Algorithms for Cloud Computing based on Virtual Machine Migration Techniques

Authors

  • Santanu Kumar Sen Gurunanak Institute of Technology, West Bengal,India
  • Sharmistha Dey West Bengal University of Technology https://orcid.org/0000-0002-8019-0937
  • Rajib Bag Supreme Knowledge Education ,India

Keywords:

Cloud, Data Center, DVFS, Load Balancing, QoS, VM Migration, Virtualization

Abstract

Green cloud is a catchphrase in today’s IT industry and hence energy efficiency in cloud computing is one of the most significant parameters to follow nowadays to evaluate the efficiency of the cloud service. It is a driving force for adaptability of a cloud computing service in recent era. For a highly commercial service like cloud, maintaining the QoS parameters and keeping the service availability and service quality highly optimized to get the competitive advantage, cloud data centers are almost available on a 24x7 basis ; which in turn is a reson for high power consumption. So it is very much necessary to maintain a balance between power and quality of the service. One feasible solution for achieving energy efficiency is Virtual Machine migration technique in real time or when they are in turned off condition. This paper discusses about several VM Migration techniques and analyses their perspectives.

Author Biographies

Santanu Kumar Sen, Gurunanak Institute of Technology, West Bengal,India

Dr. Santanu Kumar Sen, received BE(CSE), M.Tech (CSE), MBA (IS) and PhD(Engg.) from REC Silchar and Jadavpur University respectively. He is a Fellow of IET(UK), IE(I), IETE(I) and Sr. Member of IEEE (USA), CSI(I) and life members of ISTE. Presently he is working as Professor and Principal in Gurunanak Institute of Technology. He has around 25 years of experience in the field of Computer Science and Engineering in which 8 years in Industry and 17 years in Engineering Academia including Abroad.

He has got Rashtriya Shiksha Gourav Puroskar from Centre for  Education Growth and Research (CEGR) in 2016

,Academic Excellence Award – Special Leadership Award from JIS Group in 2017,Indira Gandhi Sadbhavna Award” from Global Achievers Foundation  in 2014, Bharat Bibhushan Samman Puraskar  in 2013.He has more than 70 research papers and 4 patent has been filed under his supervision.

His research interests are Computer Network, Network Security, Routing algorithms ,Cloud Computing, IoT Security, Big Data, Machine Learning, Deep Learning.

Sharmistha Dey, West Bengal University of Technology

Sharmistha Dey, received B. Sc(CU), MCA(WBUT), M Tech(CU) She is working as a research scholar in West Bengal University of Technology, West Bengal.Her research area is Cloud Security.

She has published several  papers in International journals and conferences like IEEE,Springer,MGH.

 

Her research interest is Cloud Computing, Wireless Network, Cyber Security, Machine Learning, Data Analytics, Artificial Intelligence.

Rajib Bag, Supreme Knowledge Education ,India

Dr. Rajib Bag was born in 1969, received his B.Sc (Physics Hons.) from Calcutta University, M.Sc. (Physics) from Vinoba Bhave University and M.Tech. & Ph.D (Engg.) from Jadavpur University, India in the year of 1991, 1996, 2007 & 2012 respectively. His doctoral work was in the field of control systems. Presently, he is working as a Professor & Head in the department of Computer Science & Engineering at Supreme Knowledge Foundation Group of Institutions under Maulana Abul Kalam Azad University of Technology, West Bengal, India. He has more than 40 publications in reputed refereed journals and  conference proceedings to his credit. Presently  five research scholars are doing their research work in different areas under his supervision. His research interest includes image and signal processing, education technology , machine learning, deep learning and IOT security besides control systems.

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Published

2019-07-10

How to Cite

Sen, S. K., Dey, S., & Bag, R. (2019). Study of Energy Efficient Algorithms for Cloud Computing based on Virtual Machine Migration Techniques. International Journal of Machine Learning and Networked Collaborative Engineering, 3(02), 93–101. Retrieved from https://mlnce.net/index.php/Home/article/view/79