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Task Failure Prediction using Combine Bagging Ensemble (CBE) Classification in Cloud Workflow

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Abstract

Scientific applications adopt cloud environment for executing its workflows as tasks. When a task fails, dependency nature of the workflows affects the overall performance of the execution. An efficient failure prediction mechanism is needed to execute the workflow efficiently. This paper proposes a failure prediction method which is implemented using various machine learning classifiers. Among different classifiers, Naïve Bayes predicts the failure with the highest accuracy of 94.4%. Further, to improve the accuracy of prediction, a novel ensemble method called combine bagging ensemble is introduced and acquires overall accuracy as 95.8%. The validation of proposed method is carried out by comparing simulation and real-time cloud testbed.

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Authors and Affiliations

  1. School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, India

    P. Padmakumari & A. Umamakeswari

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  1. P. Padmakumari
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  2. A. Umamakeswari
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Correspondence to P. Padmakumari.

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Padmakumari, P., Umamakeswari, A. Task Failure Prediction using Combine Bagging Ensemble (CBE) Classification in Cloud Workflow. Wireless Pers Commun 107, 23–40 (2019). https://doi.org/10.1007/s11277-019-06238-9

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