Teaching-Learning Based Task Scheduling Optimization in Cloud Computing Environments | Zendy

Ramakrishna Goddu | Zendy; R. Kiran Kumar | Zendy

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Teaching-Learning Based Task Scheduling Optimization in Cloud Computing Environments

Author(s) -

Ramakrishna Goddu,

R. Kiran Kumar

Publication year - 2019

Publication title -

international journal of recent technology and engineering (ijrte)

Language(s) - English

Resource type - Journals

ISSN - 2277-3878

DOI - 10.35940/ijrte.b2672.078219

Subject(s) - computer science , cloud computing , scheduling (production processes) , distributed computing , dynamic priority scheduling , two level scheduling , task (project management) , fair share scheduling , job shop scheduling , artificial intelligence , mathematical optimization , computer network , schedule , operating system , quality of service , mathematics , management , economics

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.

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