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

Intelligent Automation & Soft Computing 2023年第4期36卷 223-233页

作者： S.Sridevi jeevaa katiravan Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India

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.

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

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Wireless Sensor Network-based Detection of Poisonous Gases Using Principal Component Analysis

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Computer Systems Science & Engineering 2023年第1期44卷 249-264页

作者： N.Dharini jeevaa katiravan S.M.Udhaya Sankar Easwari Engineering College Chennai600089India Velammal Engineering College Chennai600066India Velammal Institute of Technology Chennai601204India

This work utilizes a statistical approach of Principal Component Ana-lysis(PCA)towards the detection of Methane(CH_(4))-Carbon Monoxide(CO)Poi-soning occurring in coal mines,forestfires,drainage systems *** the CH_(4) and CO emissions are very high in closed buildings or confined spaces during oxi-dation *** methane and carbon monoxide are highly toxic,colorless and odorless *** of the gases have their own toxic levels to be *** during their combined presence,the toxicity of the either one goes unidentified may be due to their low levels which may lead to an *** using PCA,the correlation of CO and CH_(4) data is carried out and by identifying the areas of high correlation(along the principal component axis)the explosion suppression action can be triggered earlier thus avoiding adverse effects of massive ***-less Sensor Network is deployed and simulations are carried with heterogeneous sensors(Carbon Monoxide and Methane sensors)in NS-2 Mannasim *** rise in the value of CO even when CH_(4) is below the toxic level may become hazardous to the people *** our proposed methodology will detect the combined presence of both the gases(CH_(4) and CO)and provide an early warning in order to avoid any human losses or toxic effects.

关键词： Wireless sensor network principal component analysis carbon monoxide-methane poisoning confined spaces

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