Managing Energy Consumption in Distributed Data Centers using Genetic algorithm
G H S Kaushik1, B Thirumala rao2, R Viswanath3, Keerthana J4
1G H S Kaushik , undergraduate student in Computer science engineering at KL University, Vijayawada, India.
2Dr B Thirumala rao, is working as a professor, Department of CSE, KL Deemed to be University. He completed his M.Tech and PhD degrees in Computer Science and Engineering from Acharya Nagarjuna University, Guntur, India.
3R Viswanath undergraduate student in Computer science engineering at KL University, Vijayawada, India.
4Keerthana J, undergraduate student in Computer science engineering at KL University, Vijayawada, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 6594-6597 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8562118419/2019©BEIESP | DOI: 10.35940/ijrte.D8562.118419
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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: This work shows a multi-target approach for planning vitality utilization in server farms thinking about customary and environmentally friendly power vitality information sources. Cloud computing is a developing innovation. Cloud computing offers administrations such as IaaS, SaaS, PaaS and it gives computing resources through virtualization over data network. Data center consumes huge amount of electrical energy in which it releases very high amount of carbon-di-oxide. The foremost critical challenge in cloud computing is to implement green cloud computing with the help of optimizing energy utilization. The carbon footprint is lowered while minimizing the operating cost. We know that renewable energies that are produced on-site are highly variable and unpredictable but usage of green energy is very important for the mankind using huge amount of single sourced brown energy is not suggested, so our algorithm which evolves genetically and gives practical solution in order to use renewable energy.
Keywords: Cloud Provisioning, Genetic Algorithms, Operating Cost Minimization, Self-Determining Errands, Total Cost Of Ownership.
Scope of the Article: Parallel and Distributed Algorithms.