A Centralized Multi-Criteria Method for Scheduling Tasks in a Cloud Computing Environment

Authors

  • Ehsan Shojaeian Computer Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran.
  • Mehran Mohsenzadeh Computer Engineering Department, Islamic Azad University, Science and Research Branch, Tehran, IR.
  • Mohammad Mehdi Sahrapour Business School, Monash University, 900 Dandenong Rd, Caulfield East VIC, Australia

DOI:

https://doi.org/10.53799/ajse.v22i2.506

Keywords:

Cloud computing, Scheduling the tasks with deadline, Multi-criteria prioritization, Elasticity.

Abstract

Task scheduling determines the order of mapping tasks to virtual machines to meet objectives. In this paper, a batch mode heuristic method that is centralized, dynamic, and multi-objective has been presented for scheduling independent tasks with a deadline and belonging to several user levels, using the cloud elasticity in the public cloud environment. In this method, it has been intended to improve the objectives of makespan, deadline violation, total execution cost, and load balancing by considering the tasks’ prioritization based on the criteria of user level, deadline, task length, and selection of heterogeneous virtual machines according to processing power, workload and usage cost. The proposed method was simulated using the CloudSim tool. Besides, the method’s ability to achieve the mentioned goals has been evaluated in comparison with similar methods. The evaluation results, established on standard test data, show that the proposed method has a good performance in improving its objectives.

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Published

08/22/2023

Issue

Vol. 22 No. 2 (2023): AJSE Volume 22 Issue 2

Section

Articles

License

AJSE contents are under the terms of the Creative Commons Attribution License. This permits anyone to copy, distribute, transmit and adapt the work non-commercially provided the original work and source is appropriately cited.

How to Cite

[1]
“A Centralized Multi-Criteria Method for Scheduling Tasks in a Cloud Computing Environment”, AJSE, vol. 22, no. 2, pp. 153–163, Aug. 2023, doi: 10.53799/ajse.v22i2.506.