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Failure Prediction for Scientific Workflows Using Nature-Inspired Machine Learning Approach

S. Sridevi*, Jeevaa Katiravan

Department of Computer Science and Engineering, Velammal Engineering College, Chennai, 600066, India

* Corresponding Author: S. Sridevi. Email: email

Intelligent Automation & Soft Computing 2023, 36(1), 223-233. https://doi.org/10.32604/iasc.2023.031928

Abstract

Scientific workflows have gained the emerging attention in sophisticated large-scale scientific problem-solving environments. The pay-per-use model of cloud, its scalability and dynamic deployment enables it suited for executing scientific workflow applications. Since the cloud is not a utopian environment, failures are inevitable that may result in experiencing fluctuations in the delivered performance. Though a single task failure occurs in workflow based applications, due to its task dependency nature, the reliability of the overall system will be affected drastically. Hence rather than reactive fault-tolerant approaches, proactive measures are vital in scientific workflows. This work puts forth an attempt to concentrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm (IWDA) combined with an efficient machine learning approach-Support Vector Regression (SVR) for task failure prognostication which facilitates proactive fault-tolerance in the scheduling of scientific workflow applications. The failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workflows. The experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques.

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Cite This Article

S. Sridevi and J. Katiravan, "Failure prediction for scientific workflows using nature-inspired machine learning approach," Intelligent Automation & Soft Computing, vol. 36, no.1, pp. 223–233, 2023. https://doi.org/10.32604/iasc.2023.031928



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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