Optimized Task Scheduling for Cloud Computing Using PSO and GA
B. V. Seshu Kumari1, Giri Prasad2, Somula Ramasubbareddy3, K. Govinda4

1B. V. Seshu Kumari, Associate Professor, VNRVJIET, Hyderabad (Telangana), India.
2Giri Prasad. A, Assistant Professor, VNRVJIET, Hyderabad (Telangana), India.
3Somula Ramasubbareddy, Assistant Professor, VNRVJIET, Hyderabad (Telangana), India.
4K. Govinda, Associate Professor, VIT University, Vellore (Tamil Nadu), India.
Manuscript received on 06 July 2019 | Revised Manuscript received on 16 August 2019 | Manuscript Published on 27 August 2019 | PP: 608-611 | Volume-8 Issue-2S4 July 2019 | Retrieval Number: B11200782S419/2019©BEIESP | DOI: 10.35940/ijrte.B1120.0782S419
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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: Cloud computing started with risk-free concept: Let someone else take the ownership of setting up of IT infrastructure and let end-users tap into it, paying only for what is been used. A service offering computation resources is frequently referred to as Infrastructure as a Service (IaaS) and Software as a Service (SaaS). An environment used for construction, deployment, and management of applications is called PaaS (Platform as a Service). With the advent of cloud computing, resizable infrastructure for data analysis is now available to everyone via an on-demand maybe free model. Cloud computing totally depends on the internet to deliver its services to the users. In the modern computing environment where the amount of data to be processed is increasing day by day, the costs involved in the transmission and execution of such amount of data is mounting significantly. So there is a requirement of appropriate scheduling of tasks which will help to manage the escalating costs of data intensive applications. In this paper, we propose an algorithm that uses particle swarm optimization to schedule the tasks to get maximum benefits from the resources. The benefit can be expressed in terms of increased resource usage, minimized response time, minimize overall cost incurred. We will then schedule the tasks using genetic algorithm and compare the results given by particle swarm optimization as to what extent they are similar and which gives better results. Both algorithms are heuristic algorithms hence result may vary.
Keywords: Cloud Computing, Particle Swarm Optimization, Genetic Algorithm, Objective Function.
Scope of the Article: Cloud Computing