An Event Diminishment Model to Optimize Cloud Environment
Ashish Kumar Trivedi1, Ajay Kumar Bharti2
1Ashish Kumar Trivedi*, Research Scholar, Department of Computer Science, MUIT, Lucknow, (U.P.), India.
2Dr Ajay Kumar Bharti, Professor, Department of Computer Science, MUIT, Lucknow, (U.P.), India.
Manuscript received on March 18, 2020. | Revised Manuscript received on April 02, 2020. | Manuscript published on April 30, 2020. | PP: 744-749 | Volume-9 Issue-4, April 2020. | Retrieval Number: C6091029320/2020©BEIESP | DOI: 10.35940/ijeat.C6091.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cloud computing is to compute a task assigned to a set of connections, software and services that can be utilized by the user over a network. The trending need of Cloud infrastructure has drastically scale up the energy need of data centers, which has become a critical issue. In the row also lead to high carbon emission which is not environment friendly so there is a need of energy efficient approach in cloud computing The research paper aims to reach a theoretical notion of sustainable development with proposing an incentive for reducing global warming through effective clustering techniques and methods. This paper aims to reduce cloud events by applying map reduce on large event clusters formed in cloud. The purpose of the paper is to develop a better methodology for handling the events of cloud computing and possibly clustering and reducing the similar types of events. This approach might lead to the reduction of carbon-dioxide gas (which is a greenhouse gas) by less usage of servers in cloud data centers. With the advent of IT services in cloud computing energy consumption it is necessary for the developing technology to progress towards sustainable development rather thrashing and harnessing energy from every possible means.
Keywords: Cloud Computing, Clustering, Cloud Data centers, Clustering Algorithms, K-Means Clustering, Map-Reduce, Resource Identification and Clustering.