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Analysis of Job Failure and Prediction Model for Cloud Computing Using Machine Learning.

Mohammad S Jassas1, Qusay H Mahmoud1

  • 1Department of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a failure prediction model for cloud computing to enhance dependability. The model accurately identifies job failures, improving resource efficiency and cloud service reliability.

Keywords:
Google cluster traceMustang traceRandom Forest (RF)Trinity tracecloud computingfailure predictionfault tolerance

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Reliability Engineering

Background:

  • Modern applications like smart cities and eHealth require high cloud dependability and availability.
  • Cloud environments face frequent hardware and software failures due to their scale and diversity.

Purpose of the Study:

  • To analyze job failure behavior in cloud environments.
  • To develop and evaluate a predictive model for cloud job failures.
  • To enhance cloud application efficiency and resource management.

Main Methods:

  • Analysis of failed and completed jobs using publicly available traces (Google cluster, Mustang, Trinity).
  • Development of a failure prediction model for cloud jobs.
  • Evaluation of the model's accuracy using machine learning techniques.

Main Results:

  • A significant correlation was found between unsuccessful tasks and requested resources.
  • The developed failure prediction model demonstrated high precision, recall, and F1-score.
  • Machine learning models were compared to identify the most accurate for failure prediction.

Conclusions:

  • Job failure prediction is a viable strategy to improve cloud service reliability and availability.
  • The proposed model offers a promising approach to proactively manage cloud resources and prevent failures.
  • Further solutions like optimized scheduling and priority policies can complement failure prediction for enhanced cloud services.