Cloud computing is emerging as a new paradigm of large-scale distributed computing. It is a framework for enabling convenient, on demand network access to a shared pool of computing resources. Cloud computing environments provide scalability for applications by providing virtualized resources dynamically. It offers utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. Therefore, Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact is essential. This paper discusses the various methods used to reduce energy consumption and scheduling algorithms in cloud computing.

1. Alexa Huth and James Cebula, The Basics of Cloud Computing, US-CERT 2011.
2. Jayant Baliga, Robert W. A. Ayre, Kerry Hinton, and Rodney S. Tucker, Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport, IEEE 2010.
3. Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, and Ivona Brandic, Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, ELSEVIER 2009.
4. Andreas Berl, Erol Gelenbe, Marco di Girolamo, Giovanni Giuliani, Hermann de Meer, Minh Quan Dang and Kostas Pentikousis, Energy-Efficient Cloud Computing, Computer Journal 2010.
5. Luiz André Barroso and Urs Hölzle, The Case for Energy Proportional Computing, IEEE 2007.
6. Server Energy and Efficiency Report. Technical report, 1E, 2009.
7. Ismael Solis Moreno, Jie Xu, Energy-Efficiency in Cloud Computing Environments: Towards Energy Savings without Performance Degradation.
8. F. Tusa, M. Paone, M. Villari and A. Puliafito, CLEVER: A CLoud-Enabled Virtual EnviRonment IEEE 2010.
9. G. von Laszewski, L. Wang, A. Younge, X. He, Power-aware scheduling of virtual machines in DVFS-enabled clusters, IEEE 2009.
10. Enrique V. Carrera, Eduardo Pinheiro, and Ricardo Bianchini, Conserving disk energy in network servers, ACM.
11. Sudhanva Gurumurthi, Anand Sivasubramaniam, Mahmut Kandemir, and Hubertus Franke, Drpm: Dynamic speed control for power management in server class disks, in: Computer Architecture, International Symposium 2003.
12. Ching-Hsien Hsu, Shih-Chang Chen2, Chih-Chun Lee, Hsi-Ya Chang, Kuan-Chou Lai, Kuan-Ching Li and Chunming Rong, Energy-Aware Task Consolidation Technique for Cloud Computing, IEEE 2011.
13. David P. Helmbold, Darrell D. E. Long, Tracey L. Sconyers and Bruce Sherrodm, Adaptive Spin Down For Mobile Computers, Journal on Mobile Network and Applications 2000.
14. Kyong Hoon Kim, Rajkumar Buyya, Jong Kim, SLA-Based Scheduling Of Bag-Of-Tasks Applications On Power-Aware Cluster Systems, Ieice Trans. Inf. & Syst. 2010.
15. Jiandun Li , Junjie Peng , Zhou Lei , Wu Zhang , An Energy-Efficient Scheduling Approach Based On Private Clouds, Journal of Information & Computational Science 2011.
16. Round-robin (RR) on Wikipedia: http://en. wikipedia. org/wiki/Round-robin_scheduling.
17. Greedy on Wikipedia: http://en. wikipedia. org/wiki/Greedy algorithm.
18. Anton Beloglazov, Jemal Abawajy, Rajkumar Buyya, Energy-Aware Resource Allocation Heuristics For Efficient Management Of Data Centers For Cloud Computing, ELSEVIER 2012.
19. R. Buyya, A. Beloglazov, J. Abawajy, Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges, in: International Conf on Parallel and Distributed Processing Techniques and Applications 2010.
20. Eduardo Pinheiro, Ricardo Bianchini, Enrique V. Carrera, and Taliver Heath, Load Balancing and Unbalancing for Power and Performance in Cluster-Based Systems.

Scheduling Cloud Computing Energy Efficiency Virtualization