Work place: Department of CS and IT, MJP Rohilkhand University, Bareilly, India
E-mail: rsiet2002@gmail.com
Website:
Research Interests: Computer systems and computational processes, Artificial Intelligence, Computational Learning Theory
Biography
Dr. Ravendra Singh is presently working as a Professor in Department of CS & IT, Faculty of Engineering, M.J.P. Rohilkhand University, Bareilly, India. He has more than 22 year academic experience including 17 years of research. He has authored over 90 publications and 4 books. In past he did maximum research in the field of parallel and distributed computing or computer network regarding task allocation & scheduling. Currently his research includes Artificial intelligence, Machine learning, Deep learning and its applications.
By Shailesh Saxena Mohammad Zubair Khan Ravendra Singh
DOI: https://doi.org/10.5815/ijmsc.2021.02.05, Pub. Date: 8 Jun. 2021
Cloud computing is a widely acceptable computing environment, and its services are also widely available. But the consumption of energy is one of the major issues of cloud computing as a green computing. Because many electronic resources like processing devices, storage devices in both client and server site and network computing devices like switches, routers are the main elements of energy consumption in cloud and during computation power are also required to cool the IT load in cloud computing. So due to the high consumption, cloud resources define the high energy cost during the service activities of cloud computing and contribute more carbon emissions to the atmosphere. These two issues inspired the cloud companies to develop such renewable cloud sustainability regulations to control the energy cost and the rate of CO2 emission. The main purpose of this paper is to develop a green computing environment through saving the energy of cloud resources using the specific approach of identifying the requirement of computing resources during the computation of cloud services. Only required computing resources remain ON (working state), and the rest become OFF (sleep/hibernate state) to reduce the energy uses in the cloud data centers. This approach will be more efficient than other available approaches based on cloud service scheduling or migration and virtualization of services in the cloud network. It reduces the cloud data center's energy usages by applying a power management scheme (ON/OFF) on computing resources. The proposed approach helps to convert the cloud computing in green computing through identifying an appropriate number of cloud computing resources like processing nodes, servers, disks and switches/routers during any service computation on cloud to handle the energy-saving or environmental impact.
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