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A fact based analysis of decision trees for improving reliability in cloud computing.

Muhammad Asim Shahid1,2, Muhammad Mansoor Alam1,3,4,5, Mazliham Mohd Su'ud5

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This study compares five machine learning algorithms for cloud computing fault prediction. The modified Decision Tree (J48) algorithm achieved the highest accuracy (97.07%) with minimal fault prediction errors.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Cloud Computing

Background:

  • Cloud computing (CC) adoption is rapidly increasing due to cost-effectiveness and scalability.
  • Businesses are migrating to the cloud for enhanced accessibility and transparency.
  • Accurate fault prediction is crucial for reliable cloud infrastructure.

Purpose of the Study:

  • To compare the accuracy and fault prediction capabilities of five machine learning algorithms.
  • To identify the most effective algorithm for cloud computing fault prediction.
  • To propose modifications to enhance the performance of the chosen algorithm.

Main Methods:

  • Evaluated AdaBoostM1, Bagging, Decision Tree (J48), Deep Learning (Dl4jMLP), and Naive Bayes Tree (NB Tree) algorithms.
  • Utilized secondary data analysis for initial comparisons.
  • Conducted primary data analysis using 80/20, 70/30, and 10-fold cross-validation splits.
  • Measured accuracy rates and fault prediction errors for each algorithm.

Main Results:

  • Naive Bayes Tree (NB Tree) showed high accuracy (97.05%) but poor time complexity (1.01s).
  • Decision Tree (J48) offered comparable accuracy (96.78%) with significantly better time complexity (0.11s).
  • Modified Decision Tree (J48) achieved the highest accuracy across validation splits (up to 97.07%) with low fault prediction.

Conclusions:

  • The Decision Tree (J48) algorithm, with modifications, is highly effective for cloud computing fault prediction.
  • The enhanced J48 algorithm balances high accuracy with efficient computational performance.
  • This research provides a robust solution for improving cloud system reliability.