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

作     者：S.Sridevi Jeevaa Katiravan 

作者机构：Department of Computer Science and EngineeringVelammal Engineering CollegeChennai600066India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第36卷第4期

页      面：223-233页

核心收录：

学科分类：08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Failure prediction intelligent water drops support vector regression proactive fault-tolerance scientific workflows precision accuracy resource provisioning 

摘      要：Scientific workflows have gained the emerging attention in sophisti-cated large-scale scientific problem-solving *** pay-per-use model of cloud,its scalability and dynamic deployment enables it suited for executing scientific workflow *** the cloud is not a utopian environment,failures are inevitable that may result in experiencingfluctuations in the delivered *** a single task failure occurs in workflow based applications,due to its task dependency nature,the reliability of the overall system will be affected *** rather than reactive fault-tolerant approaches,proactive measures are vital in scientific workfl*** 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 *** 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 workfl*** experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques.