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Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm.

Muhammad Asim Shahid1, Muhammad Mansoor Alam1,2,3,4, Mazliham Mohd Su'ud4

  • 1Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia.

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Summary
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This study enhanced cloud computing adoption by comparing machine learning algorithms for fault prediction. The Modified Sequential Minimal Optimization (MSMO) algorithm achieved the highest accuracy and lowest error rates, improving cloud technology accessibility.

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Area of Science:

  • Cloud Computing
  • Machine Learning
  • Data Science

Background:

  • Cloud computing offers significant benefits but faces adoption challenges due to technical difficulties.
  • Machine learning (ML) algorithms are crucial for analyzing cloud system performance and predicting faults.
  • Evaluating various ML classifiers is essential to identify optimal solutions for enhancing cloud reliability.

Purpose of the Study:

  • To compare the performance of multiple machine learning algorithms in predicting cloud computing faults.
  • To identify the most accurate and efficient classifier for improving cloud system reliability and user adoption.
  • To propose a novel algorithm that optimizes accuracy and minimizes fault prediction errors.

Main Methods:

  • Evaluated six machine learning algorithms: Naïve Bayes (NB), Library Support Vector Machine (LibSVM), Multinomial Logistic Regression (MLR), Sequential Minimal Optimization (SMO), K Nearest Neighbor (KNN), and Random Forest (RF).
  • Utilized secondary data (CPU-Mem Mono/Multi, HDD Mono/Multi) and primary data for performance evaluation.
  • Assessed classifiers based on accuracy, fault prediction rates, and algorithm complexity (time complexity).

Main Results:

  • Naïve Bayes (NB) and Sequential Minimal Optimization (SMO) showed strong performance on secondary datasets.
  • Random Forest (RF) achieved high accuracy on primary data but had poor time complexity.
  • SMO demonstrated a good balance of accuracy and efficiency, leading to the development of a modified version.

Conclusions:

  • The Modified Sequential Minimal Optimization (MSMO) algorithm was proposed, achieving superior accuracy (up to 96.50%) and minimal fault prediction errors.
  • MSMO offers a promising solution for enhancing cloud computing reliability and facilitating wider technology adoption.
  • The research highlights the importance of algorithm optimization for practical cloud system management.