A Machine Learning-Based Approach to Estimate the CPU-Burst Time for Processes in the Computational Grids

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 averag...

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Published in:2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS) pp. 3 - 8
Main Authors: Helmy, Tarek, Al-Azani, Sadam, Bin-Obaidellah, Omar
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2015
Subjects:
Artificial intelligence
Computational modeling
CPU Scheduling Algorithm
CPU-Burst
Feature Selection
Machine Learning
Scheduling algorithms
Single machine scheduling
Testing
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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.
AbstractList 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.
Author Helmy, Tarek
Bin-Obaidellah, Omar
Al-Azani, Sadam
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  organization: Dept. of Inf. & Comput. Sci., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
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Snippet 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...
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SubjectTerms Artificial intelligence
Computational modeling
CPU Scheduling Algorithm
CPU-Burst
Feature Selection
Machine Learning
Scheduling algorithms
Single machine scheduling
Testing
Title A Machine Learning-Based Approach to Estimate the CPU-Burst Time for Processes in the Computational Grids
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