scholarly journals Green Computing: An Era of Energy Saving Computing of Cloud Resources

2021 ◽  
Vol 7 (2) ◽  
pp. 42-48
Author(s):  
Shailesh Saxena ◽  
◽  
Mohammad Zubair Khan ◽  
Ravendra Singh
Author(s):  
Hyunjeong Lee ◽  
Jinsoo Han ◽  
Youn-Kwae Jeong ◽  
Il-Woo Lee

2019 ◽  
Vol 7 (3) ◽  
pp. 719-722
Author(s):  
Faiza Saghir ◽  
Shabina Ghafir

2011 ◽  
Vol 50-51 ◽  
pp. 733-737
Author(s):  
Juan Li Hu ◽  
Jia Bin Deng ◽  
Jin Bo Liu ◽  
Jue Bo Wu

Green, energy-saving, environmental protection and sustainable development have become the most popular buzzword and topic in science research. This paper presents the concept of generalized green computing and describes the limit and the range for it, including the research task and relationship. It analyzes and compares these researches on green computing. Problems of current topics are discussed, and finally future directions are proposed in this paper.


Author(s):  
Chiraparapu Srinivasa Rao ◽  
N. Naga Sarveswara Rao

The introduction of semi conductors changed the world which made our electronic devices and personal computers simple and complex this impact made many companies to introduce lot of computer organized devices which are available to commoners and every individual due to this within a years the usage of these items increased that means the material demand increased in a fast forward the pollutants from it also increased which is harming environment. So, in order to save environment green computing must be followed this topic is very important as how recycling, non-biodegradable components, eco friendly products, hazardous CFCs all comes under this including energy saving, efficiency providing things, sustainable resources. This presentation gives a general overview on the current state of opportunities which are seeing it as a way to create new profit centers while trying to help the environment cause and several formations that we can explore in it in coming future. The new electronic products and services with optimum efficiency and all possible options towards energy saving.


Author(s):  
Karuppasamy M. ◽  
Balakannan S. P.

Cloud computing services are proliferation. The Cloud computing resources face major pitfall in energy consumes. The prime of energy consumption in cloud computing is by means of client computational devices, server computational devices, network computational devices and power required to cool the IT load. The cloud resources contribute high operational energy cost and emit more carbon emission to the environment. Therefore the cloud services providers need green cloud environment resolution to decrease the operational energy cost along with environmental impact. The most important objective of this effort is to trim down the energy from utilized and unutilized (idle) cloud resources and save the energy in cloud resources efficiently. To achieve the sustainable green cloud environment from an Energy Saving Algorithm used to choose the appropriate virtual services so that the power at the client, server, and network recourses can be reduced.  


2001 ◽  
Vol 32 (3) ◽  
pp. 133-141 ◽  
Author(s):  
Gerrit Antonides ◽  
Sophia R. Wunderink

Summary: Different shapes of individual subjective discount functions were compared using real measures of willingness to accept future monetary outcomes in an experiment. The two-parameter hyperbolic discount function described the data better than three alternative one-parameter discount functions. However, the hyperbolic discount functions did not explain the common difference effect better than the classical discount function. Discount functions were also estimated from survey data of Dutch households who reported their willingness to postpone positive and negative amounts. Future positive amounts were discounted more than future negative amounts and smaller amounts were discounted more than larger amounts. Furthermore, younger people discounted more than older people. Finally, discount functions were used in explaining consumers' willingness to pay for an energy-saving durable good. In this case, the two-parameter discount model could not be estimated and the one-parameter models did not differ significantly in explaining the data.


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