Energy-Efficient Load Balancing Technique to optimize Average response time and Data Center Processing Time in Cloud Computing Environment

Energy-Efficient Load Balancing Technique to optimize Average response time and Data Center Processing Time in Cloud Computing Environment S. Jeyalaksshmi J. Anita Smiles D. Akila Dibyendu Mukherjee Ahmed J. Obaid Abstract Cloud infrastructure is a modern computing system in which pooled services are made available to users at various times depending on their demands. The process of distributing workload among computing system nodes is known as load balancing. Loads include things like CPU power; ram capacity, and network traffic. A well-balanced load avoids the situation when some nodes are fully loaded while others are inactive or not working. Where there are many tasks in a virtual machine (VM), these tasks are delegated to underutilize VMs in the same or a separate datacenter. Modified Round Robin and Modified Honey Bee Algorithms are proposed in this article for effective load balancing based on honey bee and Round Robin foraging activity to control load through VMs. Tasks taken from VMs that are crowded are viewed as honey bees. In the suggested method which is a circular ribbon, filled VMs are considered. In order to ensure a fast responding time and a minimum number of task migrations, the planned protocol also explores the aims of tasks in VM queues. The results of the test indicate that the quality of service has improved considerably (QoS).
07 01 2021 012145 http://dx.doi.org/10.1088/crossmark-policy iopscience.iop.org Energy-Efficient Load Balancing Technique to optimize Average response time and Data Center Processing Time in Cloud Computing Environment Journal of Physics: Conference Series paper Published under licence by IOP Publishing Ltd http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining 10.1088/1742-6596/1963/1/012145 https://iopscience.iop.org/article/10.1088/1742-6596/1963/1/012145 https://iopscience.iop.org/article/10.1088/1742-6596/1963/1/012145/pdf https://iopscience.iop.org/article/10.1088/1742-6596/1963/1/012145/pdf https://iopscience.iop.org/article/10.1088/1742-6596/1963/1/012145 https://iopscience.iop.org/article/10.1088/1742-6596/1963/1/012145/pdf Das 0384 2017 Geetha 806 2017 Ghosh 33 2018 Babu 67 2016 Kaur 1 2016 Patel 145 2019 Kaneria 1 2016 Shen 1131 2019 Sudhakar 605 2018 Journal of Computational and Theoretical Nanoscience Juneja 17 4408 2020 10.1166/jctn.2020.9087 Healthcare 4.0-Digitizing Healthcare Using Big Data for Performance Improvisation Jeyalaksshmi 10.1109/ICCAKM50778.2021.9357710 97 2021 Journal of Engineering Science and Technology (JESTEC) Pal 11 1282 2016 Adaptation of Johnson Sequencing for Job Scheduling to Minimize the Average Waiting Time in Cloud Computing Environment International Journal of Electrical and Computer Engineering (IJECE) Pal 6 743 2016 0.11591/ijece.v6i2.pp743-750 A Simulation-based Approach to Optimize the Execution Time and Minimization of Average Waiting Time Using Queuing Model in Cloud Computing Environment J. Phys.: Conf. Ser. Yeganegi 1530 2020 J. Phys.: Conf. Ser. Kareem 1879 2021