Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment
Ankit Tomar1, Bhaskar Pant2, Vikas Tripathi3, Priyank Pandey4, Kamal Kant Verma5

1Ankit Tomar*, CSE, Graphic Era Deemed to be University, Dehradun, India.
2Bhaskar Pant, CSE, Graphic Era Deemed to be University, Dehradun, India.
3Vikas Tripathi, CSE, Graphic Era Deemed to be University, Dehradun, India.
4Priyank Pandey, CSE, Graphic Era Deemed to be University, Dehradun, India.
5Kamal Kant Verma, CSE, College of Engineering Roorkee, India.
Manuscript received on November 23, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1232-1237 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B3707129219/2020©BEIESP | DOI: 10.35940/ijeat.B3707.129219
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Abstract: A vibrant on demand service of today’s era is cloud computing where one can utilize computer resources without indirect active management by user where one can use computing resources to achieve coherence in economic scale. Since cloud computing feel like Everything as a service so there should be highly scalable and reliable mechanisms to distribute the load evenly across the VMs evenly. Innumerable cloudlet mapping policies are presented in various research articles to achieve the high performance, better QOS and minimized task execution time but maximum are conventional approaches. No unconventional realistic scheduling algorithms is available which can schedule the tasks in heterogeneous manner. Since cloudlet scheduling is crucial metrics of cloud computing that has to be heightened by combining the different parameters. This paper tried to provide effectiveness and improvement in task scheduling using nature inspired Particle Swarm optimization (PSO) strategy. A powerful nature inspired load balancing mechanism is proposed in this paper which optimized makespan and throughput in environment of varying cloudlets and virtual machines results as compared to other conventional approaches. Proposed (EPSO) algorithm is with four scheduling policies namely FCFS, Round Robin (RR) and Shortest Job First (SJF) and get near twice good throughput percentage and minimized makespan in two different environments. Author used Cloud sim toolkit and some Open Source cloud packages to simulate the results of various scheduling components. Experimental results of various components are tested and simulated on java based Cloud Sim toolkit framework.
Keywords: Load Balancing, Particle Swarm, Cloud computing, CloudSim, Makespan, Task Scheduling.