Consolidation of VMs to Improve Energy Efficiency in Cloud Computing Environments

2015 ◽  
Author(s):  
Thiago Kenji Okada ◽  
Albert De La Fuente Vigliotti ◽  
Daniel Macedo Batista ◽  
Alfredo Goldman vel Lejbman
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Improve Energy Efficiency ◽  
Computing Environments

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Energy-Efficiency Model and Measuring Approach for Cloud Computing

2012 ◽  
Vol 23 (2) ◽  
pp. 200-214 ◽  
Author(s):  
Jie SONG ◽  
Tian-Tian LI ◽  
Zhen-Xing YAN ◽  
Jun NA ◽  
Zhi-Liang ZHU
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing

Energy Efficiency in Cloud Computing

2015 ◽  
Author(s):  
Anil Prasad Barnwal ◽  
V. S. Dixit
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing

The Research of Measuring Approach and Energy Efficiency for Hadoop Periodic Jobs

2015 ◽  
Vol 8 (1) ◽  
pp. 206-210 ◽  
Author(s):  
Yu Junyang ◽  
Hu Zhigang ◽  
Han Yuanyuan
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Saving ◽  
Energy Estimate ◽  
Cloud Platform ◽  
Periodic Tasks ◽  
Current Consumption ◽  
Reduce Energy Consumption

Current consumption of cloud computing has attracted more and more attention of scholars. The research on Hadoop as a cloud platform and its energy consumption has also received considerable attention from scholars. This paper presents a method to measure the energy consumption of jobs that run on Hadoop, and this method is used to measure the effectiveness of the implementation of periodic tasks on the platform of Hadoop. Combining with the current mainstream of energy estimate formula to conduct further analysis, this paper has reached a conclusion as how to reduce energy consumption of Hadoop by adjusting the split size or using appropriate size of workers (servers). Finally, experiments show the effectiveness of these methods as being energy-saving strategies and verify the feasibility of the methods for the measurement of periodic tasks at the same time.

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