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Issue title: Soft Computing and Intelligent Systems: Techniques and Applications
Guest editors: Sabu M. Thampi, El-Sayed M. El-Alfy and Ljiljana Trajkovic
Article type: Research Article
Authors: Rajavel, Rajkumara; * | Ravichandran, Sathish Kumara | Nagappan, Partheebanb | Venu, Sivakumarc
Affiliations: [a] Department of CSE, Christ University, Bangalore, Karnataka, India | [b] Department of CSE, Galgotias University, Greater Noida, Uttar Pradesh, India | [c] Department of CSE, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India
Correspondence: [*] Corresponding author. Rajkumar Rajavel, Department of CSE, Christ University, Bangalore, Karnataka, India. E-mail: rajkumarprt@gmail.com.
Abstract: Maintaining the quality of service (QoS) related parameters is an important issue in cloud management systems. The lack of such QoS parameters discourages cloud users from using the services of cloud service providers. The proposed task scheduling algorithms consider QoS parameters such as the latency, make-span, and load balancing to satisfy the user requirements. These parameters cannot sufficiently guarantee the desired user experience or that a task will be completed within a predetermined time. Therefore, this study considered the cost-enabled QoS-aware task (job) scheduling algorithm to enhance user satisfaction and maximize the profit of commercial cloud providers. The proposed scheduling algorithm estimates the cost-enabled QoS metrics of the virtual resources available from the unified resource layer in real-time. Moreover, the virtual machine (VM) manager frequently updates the current state-of-the art information about resources in the proposed scheduler to make appropriate decisions. Hence, the proposed approach guarantees profit for cloud providers in addition to providing QoS parameters such as make-span, cloud utilization, and cloud utility, as demonstrated through a comparison with existing time-and cost-based task scheduling algorithms.
Keywords: Cloud computing, task scheduling, Qos aware task scheduling, cost enabled scheduling, cloud utilization, cloud utility
DOI: 10.3233/JIFS-189881
Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5607-5615, 2021
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