This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Shailesh Saxena , Ankur Kumar , Rohit Johri
In terms of growing awareness about environmental impact of computing, green technology is gaining increasing importance. Green computing refers to the practice of environmentally responsible and efficient use of computing resources while maintaining economic viability and improving its performance in eco-friendly way. Green computing is an effective study in which disposing, recycling and manufacturing of computers and electronic devices is taken into consideration. The goal of green computing is to lower down the use of hazardous materials, maximize energy efficiency and popularize biodegradability or recyclability of outdated products and factory waste. Cloud computing becomes a powerful trend in the development of ICT(Information and Communication Technologies ) services. Demand on the cloud computing is continually growth that makes it changes to scope of green cloud computing. It aims to reduce energy consumption in Cloud computing while maintaining a better performance. We need green cloud computing solutions that can not only save energy, but also reduce operational costs. An architectural framework and principles that provides efficient green enhancements within a scalable Cloud computing architecture with resource provisioning and allocation algorithm for energy efficient management of cloud computing environments to improve energy efficiency of the data centre.In this paper we focus on analysis of computing in green environment.
 Yang Liu, WannengShu, and Chrish Zhang 2016 ,A Parallel Task Scheduling Optimization Algorithm Based on Clonal Operator in Green Cloud Computing,Journal of Communications Vol. 11, No2, February 2016
 Bianchini, R., and Rajamony, R. 2004, Power and energy management for server systems, Computer, 37 (11) 68-74.
 Rivoire, S., Shah, M. A., Ranganathan, P., and Kozyrakis, C. 2007. Joulesort: a balanced energy-efficiency benchmark, Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, NY, USA. )
 F. Q. Zhao, G. Q. Li, C. Yang, et al., “A human–computer cooperative particle swarm optimization based immune algorithm for layout design,” Neurocomputing, vol. 132, no. 5, pp. 68-78, May 2014.
 Q. Zheng, B. Veeravalli, and C. K. Tham, “On the design of fault tolerant strategies using primary-back up approach for computational grids with replication costs,” IEEE Transactions on Computers, vol. 58, no. 3, pp. 380-393, March 2009.
 J. William, L. Walter, and P. Louis, “Characterization of bandwidth-aware-meta- schedulers for co-allocating jobs across multiple clusters,” Journal of supercomputing, vol. 34, no. 2, pp. 135-163, Feb. 2005.
 L. J. Shen, L. Li, L. Rui, et al., “Computing task scheduling based on improved immune evolutionary algorithm of cloud,” Journal of Computer Engineering, vol. 38, no. 9, pp. 208-210, Sept. 2009.
 Q. Zhang, Q. Y. Zhu, and R. Boutaba, “Dynamic resource allocation for spot markets in cloud computing environments,” in Proc. 4th IEEE International Conference on Utility and Cloud Computing, Melbourne, Australia, 2011, pp. 178-185.
 W. F. Wang and S. J. Mei, “A cloud computing task scheduling strategy environment,” Electronic Technology, vol. 12, no. 7, pp. 35-38, July 2012.
 B. Mondal, K. Dasgupta, and P. Dutta, “Load balancing in cloud computing using stochastic hill Climbing-A soft computing approach,” Proceeded Technology, vol. 4, no. 5, pp. 783-789, May 2012
 Saurabh Kumar Garg and RajkumarBuyya,Green Cloud computing and Environmental Sustainability,Cloud computing and Distributed Systems (CLOUDS) Laboratory,Australia.
Department of CSE SRMS CET&R, Bareilly
No. of Downloads: 7 | No. of Views: 659