Article: Fault prediction for distributed computing Hadoop clusters using real-time higher order differential inputs to SVM: Zedacross Journal: International Journal of Information and Computer Security (IJICS) 2020 Vol.12 No.2/3 pp.181 - 198 Abstract: Hadoop distributed computing clusters are used worldwide for high-performance computations. Often various hardware and software faults occur, leading to both data and computation time losses. This paper proposes the usage of a fault prediction software called 'Zedacross' which uses machine learning principles combined with cluster monitoring tools. Firstly, the paper suggests a model that uses the resource usage statistics of a normally functioning Hadoop cluster to create a machine learning model that can then be used to predict and detect faults in real time. Secondly, the paper explains the novel idea of using higher order differentials as inputs to SVM for highly accurate fault predictions. Predictions of system faults by observing system resource usage statistics in real-time with minimum delay will play a vital role in deciding the need for job rescheduling tasks or even dynamic up-scaling of the cluster. To demonstrate the effectiveness of the design a Java utility was built to perform cluster fault monitoring. The results obtained after running the system on various test cases demonstrate that the proposed method is accurate and effective. Inderscience Publishers - linking academia, business and industry through research

Title: Fault prediction for distributed computing Hadoop clusters using real-time higher order differential inputs to SVM: Zedacross

Authors: Joey Pinto; Pooja Jain; Tapan Kumar

Addresses: Indian Institute of Information Technology, Kota, 1st Floor Prabha Bhavan, Campus MNIT Jaipur, JLN Marg, Jaipur 302017, India ' Indian Institute of Information Technology, Kota, 1st Floor Prabha Bhavan, Campus MNIT Jaipur, JLN Marg, Jaipur 302017, India ' Indian Institute of Information Technology, Kota, 1st Floor Prabha Bhavan, Campus MNIT Jaipur, JLN Marg, Jaipur 302017, India

Abstract: Hadoop distributed computing clusters are used worldwide for high-performance computations. Often various hardware and software faults occur, leading to both data and computation time losses. This paper proposes the usage of a fault prediction software called 'Zedacross' which uses machine learning principles combined with cluster monitoring tools. Firstly, the paper suggests a model that uses the resource usage statistics of a normally functioning Hadoop cluster to create a machine learning model that can then be used to predict and detect faults in real time. Secondly, the paper explains the novel idea of using higher order differentials as inputs to SVM for highly accurate fault predictions. Predictions of system faults by observing system resource usage statistics in real-time with minimum delay will play a vital role in deciding the need for job rescheduling tasks or even dynamic up-scaling of the cluster. To demonstrate the effectiveness of the design a Java utility was built to perform cluster fault monitoring. The results obtained after running the system on various test cases demonstrate that the proposed method is accurate and effective.

Keywords: fault prediction; Ganglia; Hadoop; higher order differential; SVM.

DOI: 10.1504/IJICS.2020.105155

International Journal of Information and Computer Security, 2020 Vol.12 No.2/3, pp.181 - 198

Received: 09 Oct 2017
Accepted: 23 Feb 2018
Published online: 14 Feb 2020 *

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Title: Fault prediction for distributed computing Hadoop clusters using real-time higher order differential inputs to SVM: Zedacross

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