This paper is published in Volume-3, Issue-4, 2017
Area
Cloud Computing
Author
Harjot Kaur, Sharvan Kumar
Org/Univ
KIIT College of Engineering, Gurugram, Haryana, India
Keywords
Cloud Computing, Particle Swarm Optimization, Ant colony Optimization, CloudSim
Citations
IEEE
Harjot Kaur, Sharvan Kumar. Optimize Cloud Resources Framework for Workflow Scheduling By Swarm Intelligence, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Harjot Kaur, Sharvan Kumar (2017). Optimize Cloud Resources Framework for Workflow Scheduling By Swarm Intelligence. International Journal of Advance Research, Ideas and Innovations in Technology, 3(4) www.IJARIIT.com.
MLA
Harjot Kaur, Sharvan Kumar. "Optimize Cloud Resources Framework for Workflow Scheduling By Swarm Intelligence." International Journal of Advance Research, Ideas and Innovations in Technology 3.4 (2017). www.IJARIIT.com.
Harjot Kaur, Sharvan Kumar. Optimize Cloud Resources Framework for Workflow Scheduling By Swarm Intelligence, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Harjot Kaur, Sharvan Kumar (2017). Optimize Cloud Resources Framework for Workflow Scheduling By Swarm Intelligence. International Journal of Advance Research, Ideas and Innovations in Technology, 3(4) www.IJARIIT.com.
MLA
Harjot Kaur, Sharvan Kumar. "Optimize Cloud Resources Framework for Workflow Scheduling By Swarm Intelligence." International Journal of Advance Research, Ideas and Innovations in Technology 3.4 (2017). www.IJARIIT.com.
Abstract
To completely misuse the utilizations of cloud, different difficulties should be tended to where planning is one among them. Albeit catholic research has been done on Workflow Scheduling, there are not very many edges customized for Cloud environments. For some essential standards of Cloud, for example, flexibility and heterogeneity existing work neglects to meet ideal arrangement. Hence our work concentrates on the booking techniques for logical work process on IaaS cloud. We display a calculation in view of the metaheuristic optimization system where the best of two calculations Ant colony Optimization (ACO) and Particle Swarm Optimization (PSO) are converged to enhance locally and internationally which limits the general work process time (makespan) and diminishes the cost. Our heuristic is assessed utilizing CloudSim and a few understood logical work processes of various sizes. The outcomes demonstrate that our approach performs better when contrasted with PSO calculation.