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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2019 Vol.9(1): 98-102 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.1.771

Energy Efficiency in Cloud Computing

Mohamed Deiab, Deena El-Menshawy, Salma El-Abd, Ahmad Mostafa, and M. Samir Abou El-Seoud
Abstract—Cloud computing is one of the recent emerging technologies that provides services to consumers in a pay as you go model. Cloud computing offers ITC based services over the internet and the use of virtualization allows it to provide computing resources. Data Centers are the core of cloud computing, which consists of: networked servers, cables, power sources, etc. which host the running applications and store Business information. High performance has always been the most critical concern in cloud data centers, which comes at the cost of energy consumption. The vital challenge is balancing between system performance and power consumption by reducing energy consumption without prejudicial impact on the performance and quality of services delivered. There are many techniques and algorithms proposed to achieve efficient energy utilization in cloud computing, these techniques include: VM Migration, Consolidation and Resources orchestration in cloud computing. This paper provides a survey of approaches and techniques for energy efficiency in cloud computing.

Index Terms—Cloud computing, energy efficiency, resource management, virtualization.

The authors are with the British University, Egypt (e-mail: mohamed.deiab@bue.edu.eg, deena.elmenshawy@bue.edu.eg, salma.elabd@bue.edu.eg, ahmad.mostafa@bue.edu.eg, samir.elseoud@bue.edu.eg).

[PDF]

Cite: Mohamed Deiab, Deena El-Menshawy, Salma El-Abd, Ahmad Mostafa, and M. Samir Abou El-Seoud, "Energy Efficiency in Cloud Computing," International Journal of Machine Learning and Computing vol. 9, no. 1, pp. 98-102, 2019.

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