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

作　　者：S.Sridevi Jeevaa Katiravan 

机构地区：[1]Department of Computer Science and Engineering,Velammal Engineering College,Chennai,600066,India

出　　处：《Intelligent Automation & Soft Computing》2023年第4期223-233,共11页智能自动化与软计算（英文）

摘　　要：Scientific workflows have gained the emerging attention in sophisti-cated 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 experiencingfluctuations 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 con-centrate 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 prognostica-tion 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.

关 键 词：Failure prediction intelligent water drops support vector regression proactive fault-tolerance scientific workflows precision accuracy resource provisioning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]