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Al-Maytami, BA, Fan, P, Hussain, A, Baker, T and Liatsis, P (2019) A Task Scheduling Algorithm with Improved Makespan Based on Prediction of Tasks Computation Time algorithm for Cloud Computing. IEEE Access, 7. pp. 160916-160926. ISSN 2169-3536
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A Task Scheduling Algorithm with Improved Makespan Based on Prediction of Tasks Computation Time algorithm for Cloud Computing.pdf - Published Version Available under License Creative Commons Attribution. Download (4MB) | Preview |
Abstract
Cloud computing is extensively used in a variety of applications and domains, however task and resource scheduling remains an area that requires improvement. Put simply, in a heterogeneous computing system, task scheduling algorithms, which allow the transfer of incoming tasks to machines, are needed to satisfy high performance data mapping requirements. The appropriate mapping between resources and tasks reduces makespan and maximises resource utilisation. In this contribution, we present a novel scheduling algorithm using Directed Acyclic Graph (DAG) based on the Prediction of Tasks Computation Time algorithm (PTCT) to estimate the preeminent scheduling algorithm for prominent cloud data. In addition, the proposed algorithm provides a significant improvement with respect to the makespan and reduces the computation and complexity via employing Principle Components Analysis (PCA) and reducing the Expected Time to Compute (ETC) matrix. Simulation results confirm the superior performance of the algorithm for heterogeneous systems in terms of efficiency, speedup and schedule length ratio, when compared to the state-of-the-art Min-Min, Max-Min, QoS-Guide and MiM-MaM scheduling algorithms.
| Item Type: | Article |
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| Additional Information: | © 2019 IEEE |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Date Deposited: | 14 Oct 2019 10:54 |
| Last Modified: | 04 Sep 2021 08:39 |
| DOI or ID number: | 10.1109/ACCESS.2019.2948704 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/11571 |
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