<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Cloud Data centers have adopted virtualization techniques for effective and efficient compilation of an application. The requirements of application from the execution perspective are fulfilled by scaling up and down the Virtual Machines (VMs). The appropriate selection of VMs to handle the unpredictable peak workload without load imbalance is a critical challenge for a cloud data center. In this article, we propose Pareto based Greedy-Non dominated Sorting Genetic Algorithm-II (G-NSGA2) for agile selection of a virtual machine. Our strategy generates Pareto optimal solutions for fair distribution of cloud workloads among the set of virtual machines. True Pareto fronts generate approximate optimal trade off solution for multiple conflicting objectives rather than aggregating all objectives to obtain single trade off solution. The objectives of our study are to minimize the response time, operational cost and energy consumption of the virtual machine. The simulation results evaluate that our hybrid NSGA-II outperforms as compared to the standard NSGA-II Multiobjective optimization problem.</p></abstract>

Cloud Data centers have adopted virtualization techniques for effective and efficient compilation of an application. The requirements of application from the execution perspective are fulfilled by scaling up and down the Virtual Machines (VMs). The appropriate selection of VMs to handle the unpredictable peak workload without load imbalance is a critical challenge for a cloud data center. In this article, we propose Pareto based Greedy-Non dominated Sorting Genetic Algorithm-II (G-NSGA2) for agile selection of a virtual machine. Our strategy generates Pareto optimal solutions for fair distribution of cloud workloads among the set of virtual machines. True Pareto fronts generate approximate optimal trade off solution for multiple conflicting objectives rather than aggregating all objectives to obtain single trade off solution. The objectives of our study are to minimize the response time, operational cost and energy consumption of the virtual machine. The simulation results evaluate that our hybrid NSGA-II outperforms as compared to the standard NSGA-II Multiobjective optimization problem.

Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre

Sathyabama Institute of Science and Technology

Bharati Vidyapeeth’s College of Engineering for Women

Bharati Vidyapeeth University College of Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre"}

Cloud Data centers have adopted virtualization techniques for effective and efficient compilation of an application. The requirements of application from the...

Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre

Pubblicato online: 19 set 2018

Pagine: 23 - 36

Ricevuto: 06 lug 2018

Accettato: 27 ago 2018

DOI: https://doi.org/10.2478/cait-2018-0036

Parole chiave
Cloud computing, data center, virtual machine, pareto optimal, NSGA-II, greedy

© 2018 Ketaki Bhalchandra Naik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre

Pubblicato online: 19 set 2018

Pagine: 23 - 36

Ricevuto: 06 lug 2018

Accettato: 27 ago 2018

DOI: https://doi.org/10.2478/cait-2018-0036

Parole chiaveCloud computing, data center, virtual machine, pareto optimal, NSGA-II, greedy

© 2018 Ketaki Bhalchandra Naik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Cloud computing, data center, virtual machine, pareto optimal, NSGA-II, greedy