Paper Title : IMPROVE WORKFLOW SCHEDULING TECHNIQUE USING SEMO IN CLOUD COMPUTING

ISSN : 2394-2231
Year of Publication : 2022

10.5281/zenodo.6463364

Authors: Ms. K.E. Eswari M.C.A., M.Phil., M.E., SET., U. Naveenchandar

         



MLA Style: IMPROVE WORKFLOW SCHEDULING TECHNIQUE USING SEMO IN CLOUD COMPUTING "Ms. K.E. Eswari M.C.A., M.Phil., M.E., SET., U. Naveenchandar" Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: IMPROVE WORKFLOW SCHEDULING TECHNIQUE USING SEMO IN CLOUD COMPUTING "Ms. K.E. Eswari M.C.A., M.Phil., M.E., SET., U. Naveenchandar" Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
In the cloud environment, the workflows have been frequently used to model large-scale problems in areas such as bioinformatics, astronomy, physics and arithmetic process. Such a resource obtains a task from the cloud providers that has ever-growing data and computing requirements and therefore demand a high-performance computing environment in order to be executed in a reasonable amount of time. These workflows are commonly modeled as a set of tasks interconnected via data or computing dependencies.Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow scheduling problem has been widely studied, there are very few initiatives tailored for cloud environments. Furthermore, the existing works fail to either meet the user’s Quality of Service (QoS) requirements or to incorporate some basic principles of cloud computing such as the elasticity and heterogeneity of the computing resources. This project proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) and Platform as services clouds (PaaS). This project presents an algorithm based on the Superior Element Multitude Optimization (SEMO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. The main scope of the project is used to analyze best available resource in the cloud environment depend upon the total execution time and total execution cost which is compare between one process to another process. If the provider satisfies the time least time, then the process becomes to termination.

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Keywords
— Cloud Computing, Resource Provisioning, Particle Swarm Optimization.