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Related Experiment Video

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Fault classification in the architecture of virtual machine using deep learning.

Ajay Rawat1, Anupama Mishra2, Hind Alsharif3

  • 1UK Research Innovation, Swindon, UK.

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Summary

This study introduces a novel model for predicting virtual machine failures in cluster networks. The model accurately classifies faults, enhancing network reliability and service availability.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Machine Learning

Background:

  • Network performance is critically dependent on fault probability and service availability.
  • Frequent cluster network faults can lead to service detection and identification failures.
  • Proactive fault detection and classification are essential to prevent system failures.

Purpose of the Study:

  • To develop and evaluate a model for accurate fault classification in cluster networks.
  • To predict potential failures in virtual machines to ensure service continuity.
  • To improve the overall reliability and performance of network services.

Main Methods:

  • A novel model incorporating feature selection, an attention transformer, and a feature transformer was developed.
  • The model was designed to process tabular data using neural networks.
  • Experimental analysis was conducted on a tabular dataset from the Telstra cluster network.

Main Results:

  • The proposed model achieved high performance metrics: 98.3% accuracy, 98.3% precision, 97.4% recall, and 97.8% F1 score.
  • The model effectively identified and classified fault occurrences, including service disruption events and connectivity interruptions.
  • Trace-driven experiments validated the model's efficacy in predicting virtual machine failures.

Conclusions:

  • The developed model demonstrates significant success in predicting network failures.
  • The findings validate the research objective of failure occurrence prediction in virtual machines.
  • The proposed approach offers a robust solution for enhancing network fault management and service availability.