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 language | English |
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Title of host publication | Proceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation |
Editors | Mohd Hanafi Ahmad Hijazi, Ismail Saad, David Al-Dabass, Nurmin Bolong |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3-8 |
Number of pages | 6 |
ISBN (Electronic) | 9781467386753 |
DOIs | |
State | Published - 20 Oct 2016 |
Publication series
Name | Proceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation |
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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