A. Bharanidharan, RAJ.Jahashri, K. Srinivasan, Tarun V Radhakrishnani, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, India.
International Journal of Advanced Information and Communication Technology
Received On : October 10, 2020
Revised On : November 20, 2020
Accepted On : December 15, 2020
Published On : Junuary 05, 2020
Volume 07, Issue 01
Pages : 016-019
Abstract
An effective method for the reduction of execution overhead and for improving the computational granularity of scientific workflow tasks that are executing on distributed resources is Task clustering. A job is composed of many tasks and may have a higher risk of suffering from failures than in executing a single task job. In this paper, we direct a hypothetical investigation of the effect of transient failures on the runtime execution of logical work process executions .This system proposes a maximum likelihood estimation-based parameter algorithm which is used for a general task failure modeling framework to model the workflow performance. In this paper, the system proposed here is Dynamic Balanced clustering method which combines the methods of vertical clustering, horizontal clustering and dynamic clustering to reduce the execution overhead for the scientific workflow task execution.
Keywords
Scientific Workflows; Task Clustering; Fault Tolerance; Parameter Estimation.
Cite this article
A. Bharanidharan, RAJ.Jahashri, K. Srinivasan, Tarun V Radhakrishnani, “An Efficient Fault Tolerant Clustering for Scientific Workflow,” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp. 16–19, Jan. 2020.
Copyright
© 2020 A. Bharanidharan, RAJ.Jahashri, K. Srinivasan, Tarun V Radhakrishnani. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.