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An Enhanced Task Scheduling in Cloud Computing Based on Deadline-Aware Model

An Enhanced Task Scheduling in Cloud Computing Based on Deadline-Aware Model

Mokhtar A. Alworafi, Suresha Mallappa
Copyright: © 2018 |Volume: 10 |Issue: 1 |Pages: 23
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781522543367|DOI: 10.4018/IJGHPC.2018010103
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MLA

Alworafi, Mokhtar A., and Suresha Mallappa. "An Enhanced Task Scheduling in Cloud Computing Based on Deadline-Aware Model." IJGHPC vol.10, no.1 2018: pp.31-53. http://doi.org/10.4018/IJGHPC.2018010103

APA

Alworafi, M. A. & Mallappa, S. (2018). An Enhanced Task Scheduling in Cloud Computing Based on Deadline-Aware Model. International Journal of Grid and High Performance Computing (IJGHPC), 10(1), 31-53. http://doi.org/10.4018/IJGHPC.2018010103

Chicago

Alworafi, Mokhtar A., and Suresha Mallappa. "An Enhanced Task Scheduling in Cloud Computing Based on Deadline-Aware Model," International Journal of Grid and High Performance Computing (IJGHPC) 10, no.1: 31-53. http://doi.org/10.4018/IJGHPC.2018010103

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Abstract

Cloud computing is the latest in distributed computing technology. The delivery mechanism between the service provider and users depends on Service Level Agreement (SLA). SLA contains Quality of Service (QoS), which has some constraints such as deadline to achieve user satisfaction. In this article, the authors propose a Deadline-Aware Priority Scheduling (DAPS) model to minimize the average makespan, and maximize resource utilization under deadline constraint. In the proposed model, the tasks are sorted based on length priority in ascending order and labeling the VM's state as successful which achieves the deadline constraint, and then mapping the tasks to the suitable VM that has minimum processing time. The authors compared their proposed model to the existing algorithms GA, Min-Min, SJF and Round Robin. The proposed model outperforms other algorithms by reducing the average of makespan, mean of total average response time, number of violations, violation ratio, and failure ratio, while increasing resource utilization, and guarantee ratio for tasks that meet deadline constraint.

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