Teaching-Learning Based Task Scheduling Optimization in Cloud Computing Environments
Ramakrishna Goddu1, Kiran Kumar Reddi2 

1Ramakrishna Goddu, Research Scholar, Department of Computer Science, Krishna University, Machilipatanam, Andhra Pradesh, India
2Kiran Kumar Reddi, Assistant Professor, Department of Computer Science, Krishna University, Machilipatanam, Andhra Pradesh, India

Manuscript received on 07 March 2019 | Revised Manuscript received on 15 March 2019 | Manuscript published on 30 July 2019 | PP: 2952-2958 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2672078219/19©BEIESP | DOI: 10.35940/ijrteB2672.078219
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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: Generating optimal task scheduling plans in cloud environments is a tedious task as it is a np-hard problem. The optimal resource allocation in cloud environments involves more search space and time consuming. Therefore, recent researchers are focused on implementation of artificial intelligence to solve task scheduling problem. In this paper, a new and efficient evolutionary algorithm named teaching-learning based algorithm has been implemented first time to solve the task scheduling problem in cloud environments. The current research work considers the task scheduling problem as a multi-objective optimization problem. The proposed algorithm finds the best solution by minimizing the execution time and response time while maximizing the throughput of all resources to complete the assigned tasks.
Index Terms: Teaching-Learning Based Optimization, task Scheduling, Cloud Computing, Multi-Objective Optimization, Resource Allocation and Throughput.

Scope of the Article: Cloud Computing