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Impact of Energy-Efficient and Eco-Friendly Green Computing

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Shivangi Sharma, Gaurav Sharma

Shivangi Sharma and Gaurav Sharma. Impact of Energy-Efficient and Eco-Friendly Green Computing. International Journal of Computer Applications 143(7):20-28, June 2016. BibTeX

	author = {Shivangi Sharma and Gaurav Sharma},
	title = {Impact of Energy-Efficient and Eco-Friendly Green Computing},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {143},
	number = {7},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {20-28},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2016910250},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


With the abrupt advancement in technology, every business organization aspires to migrate to cloud. The technology of cloud computing makes use of the huge data centers that present multiple issues such as extensive amounts of energy consumption, dissipation of lots of heat and methane, carbon dioxide, etc. like deadly green-house compounds and gases. In order to solve all the above stated issues of cloud computing, green computing came out as an improvement over the conventional cloud technology with energy-efficiency, security and environmental-friendliness. The two of the Linear power model and the Low Powerblade model are the existing ones, which employ four Virtual Machine scheduling algorithms for the calculation of consumption of power, are not very eco-friendly and energy efficient. These limitations of the two power models call for the requirement of a better and more efficient power model. Therefore, the research work is motivated to devise such a power model that ensures energy-efficient results and proves it environment-friendly when applied on the data centers in a secure way when compared with the two existing power models on the GreenCloud Simulator.


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Green Computing, Power models, Scheduling algorithms, GreenCloud Simulator, Energy efficiency, Virtualization, Virtual Machine Migration, Load Balancing, Server Consolidation.