Title: Support vector machine-based proactive fault-tolerant scheduling for grid computing environment

Authors: A. Shamila Ebenezer; Elijah Blessing Rajsingh; Baskaran Kaliaperumal

Addresses: Department of Computer Sciences Technology, Karunya University, Coimbatore, 641114, India ' Department of Computer Sciences Technology, Karunya University, Coimbatore, 641114, India ' Department of Electrical and Electronics Engineering, Government College of Technology, Coimbatore, 641013, India

Abstract: To classify the reliable resources accurately and perform a proactive fault tolerant scheduling in grid computing environment, a combination of support vector machine (SVM) with the quantum-behaved particle swarm optimization using Gaussian distributed local attractor point (GAQPSO) is proposed in this paper. When tuned with appropriate kernel parameters, the SVM classifier provides high accuracy in reliable resource prediction. The higher diversity of GAQPSO compared to other variants of QPSO, reduces the makespan of the schedule significantly. The performance of the SVM-GAQPSO scheduler is analysed in terms of the makespan, reliability, and accuracy. The empirical result shows that the reliability of the SVM-GAQPSO scheduler is 14% higher than the average reliability of the compared algorithms. Also, the accuracy of prediction using the SVM classifier is 92.55% and it is 37.2% high compared to classification and regression trees (CART), linear discriminant analysis (LDA), K-nearest neighbourhood (K-NN), and random forest (RF) algorithm.

Keywords: SVM classification algorithm; particle swarm optimisation; PSO; proactive fault tolerance; failure data analytics; grid computing.

DOI: 10.1504/IJAIP.2020.107539

International Journal of Advanced Intelligence Paradigms, 2020 Vol.16 No.3/4, pp.381 - 403

Received: 14 Jul 2017
Accepted: 22 Oct 2017
Published online: 01 Jun 2020 *

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