A machine learning-based approach to estimate the CPU-burst time for processes in the computational grids

Tarek Helmy*, Sadam Al-Azani, Omar Bin-Obaidellah

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

The implementation of CPU-Scheduling algorithms such as Shortest-Job-First (SJF) and Shortest Remaining Time First (SRTF) is relying on knowing the length of the CPU-bursts for processes in the ready queue. There are several methods to predict the length of the CPU-bursts, such as exponential averaging method, however these methods may not give an accurate or reliable predicted values. In this paper, we will propose a Machine Learning (ML) based approach to estimate the length of the CPU-bursts for processes. The proposed approach aims to select the most significant attributes of the process using feature selection techniques and then predicts the CPU-burst for the process in the grid. ML techniques such as Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN), Artificial Neural Networks (ANN) and Decision Trees (DT) are used to test and evaluate the proposed approach using a grid workload dataset named 'GWA-T-4 Auver Grid'. The experimental results show that there is a strength linear relationship between the process attributes and the burst CPU time. Moreover, K-NN performs better in nearly all approaches in terms of CC and RAE. Furthermore, applying attribute selection techniques improves the performance in terms of space, time and estimation.

Original languageEnglish
Title of host publicationProceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation
EditorsMohd Hanafi Ahmad Hijazi, Ismail Saad, David Al-Dabass, Nurmin Bolong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3-8
Number of pages6
ISBN (Electronic)9781467386753
DOIs
StatePublished - 20 Oct 2016

Publication series

NameProceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • CPU Scheduling Algorithm
  • CPU-Burst
  • Feature Selection
  • Machine Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Modeling and Simulation

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