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

Main Article Content

Ehsan Shojaeian
Mehran Mohsenzadeh
Mohammad Mehdi Sahrapour


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.

Article Details

How to Cite
ShojaeianE., MohsenzadehM., and SahrapourM. M., “A Centralized Multi-Criteria Method for Scheduling Tasks in a Cloud Computing Environment”, AJSE, vol. 22, no. 2, pp. 153 - 163, Aug. 2023.


[1] P. Mell and T. Grance, “The NIST definition of cloud computing,” National Institute of Standards and Technology, 2011, doi: 10.6028/NIST.SP.800-145.
[2] H. Ji et al., “Adaptive workflow scheduling for diverse objectives in cloud environments,” Transactions on Emerging Telecommunications Technologies, Vol.28, No.2, pp. e2941, 2015, doi: 10.1002/ett.2941.
[3] A.V. Lakra and D.K. Yadav, “Multi-objective tasks scheduling algorithm for cloud computing throughput optimization,” Procedia Computer Science, Vol.48, pp. 107-113, 2015, doi: 10.1016/j.procs.2015.04.158.
[4] D. Kaur and T. Sharma, “Scheduling Algorithms in Cloud Computing,” International Journal of Computer Applications, Vol.178, No.9, pp. 16-21, 2019, doi: 10.5120/ijca2019918801.
[5] J. Samriya and N. Kumar, “A QoS Aware FTOPSIS-WOA based task scheduling algorithm with load balancing technique for the cloud computing environment,” Indian Journal of Science and Technology, Vol.13, No.35, pp. 3675-3684, 2020, doi: 10.17485/IJST/v13i35.1314.
[6] S. Xue et al., “QET: a QoS-based energy-aware task scheduling method in cloud environment,” Cluster Computing, Vol.20, No.4, pp. 3199-3212, 2017, doi: 10.1007/s10586-017-1047-5.
[7] A. Chhabra et al., “QoS-Aware Energy-Efficient Task Scheduling on HPC Cloud Infrastructures Using Swarm-Intelligence Meta-Heuristics,” CMC-COMPUTERS MATERIALS & CONTINUA, Vol.64, No.2, pp. 813-834, 2020, doi: 10.32604/cmc.2020.010934.
[8] S. Mira, “Task Scheduling Balancing User Experience and Resource Utilization on Cloud,” A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Software Engineering, Rochester Institute of Technology, 2019.
[9] R. Khorsand and M. Ramezanpour, “An energy-efficient task-scheduling algorithm based on a multi-criteria decision-making method in cloud computing,” International Journal of Communication Systems, Vol.33, No.9, pp. e4379, 2020, doi: 10.1002/dac.4379.
[10] G. Muthusamy and S.R. Chandran, “Cluster-based Task Scheduling Using K-Means Clustering for Load Balancing in Cloud Datacenters,” Journal of Internet Technology, Vol.22, No.1, pp. 121-130, 2021.
[11] S.E. Shukri et al., “Enhanced multi-verse optimizer for task scheduling in cloud computing environments,” Expert Systems with Applications, Vol.168, pp. 114230, 2020, doi: 10.1016/j.eswa.2020.114230.
[12] M. Hussain et al., “Energy and Performance-Efficient Task Scheduling in Heterogeneous Virtualized Cloud Computing,” Sustainable Computing: Informatics and Systems, Vol.30, pp. 100517, 2021, doi: 10.1016/j.suscom.2021.100517.
[13] A. Gupta et al., “Load balancing based hyper heuristic algorithm for cloud task scheduling,” Journal of Ambient Intelligence and Humanized Computing, 2020, doi: 10.1007/s12652-022-04238-5.
[14] H. Yuan et al., “Revenue and energy cost-optimized biobjective task scheduling for green cloud data centers,” IEEE Transactions on Automation Science and Engineering, pp. 1-14, 2020, doi: 10.1109/TASE.2020.2971512.
[15] A. Thomas et al., “Credit Based Scheduling Algorithm in Cloud Computing Environment,” Procedia Computer Science, Vol.46, pp. 913-920, 2015, doi: 10.1016/j.procs.2015.02.162.
[16] O. Elzeki et al., “Overview of scheduling tasks in distributed computing systems,” International Journal of Soft Computing and Engineering (IJSCE), Vol.2, No.3, pp. 470-475, 2012.
[17] S. Mohapatra et al., “A Comparative Study of Task Scheduling Algorithm in Cloud Computing,” (Springer, edn.), pp. 325-338, 2020, doi: 10.1007/978-981-15-1483-8_28.
[18] S. Devipriya and C. Ramesh, “Improved max-min heuristic model for task scheduling in cloud,” (IEEE, edn.), pp. 883-888, 2013, doi: 10.1109/ICGCE.2013.6823559.
[19] T. Mathew et al., “Study and analysis of various task scheduling algorithms in the cloud computing environment,” (IEEE, edn.), pp. 658-664, 2014, doi: 10.1109/ICACCI.2014.6968517.
[20] V. Poonam Chaudhary, “Deadline and Suffrage Aware Task Scheduling Approach for Cloud Environment,” International Research Journal of Engineering and Technology, Vol.4, No.8, pp. 972-977, 2017.
[21] X. Wu et al., “A task scheduling algorithm based on QoS-driven in cloud computing,” Procedia Computer Science, Vol.17, pp. 1162-1169, 2013, doi: 10.1016/j.procs.2013.05.148.
[22] R.N. Calheiros et al., “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software: Practice and Experience, Vol.41, No.1, pp. 23-50, 2011, doi: 10.1002/spe.995.
[23] N. Rajak and D. Shukla, “A Systematic Analysis of Task Scheduling Algorithms in Cloud Computing,” (Springer, edn.), pp. 39-49, 2020, doi: 10.1007/978-981-15-2071-6_4.
[24] R. Gulbaz, “Task Scheduling Optimization in Cloud Computing,” A thesis submitted in partial fulfillment for the degree of Master of Science, Capital University of Science and Technology, 2020.
[25] R. Chen et al., “A Cloud Task Scheduling Algorithm Based on Users' Satisfaction,” (IEEE, edn.), pp. 1-5, 2013, doi: 10.1109/ICNDC.2013.11.
[26] H. Han et al., “A Qos Guided task Scheduling Model in cloud computing environment,” (IEEE, edn.), pp. 72-76, 2013, doi: 10.1109/EIDWT.2013.17.
[27] Y. Fan et al., “Executing Time and Cost-Aware Task Scheduling in Hybrid Cloud Using a Modified DE Algorithm,” (Springer, edn.), pp. 74-83, 2015, doi: 10.1007/978-981-10-0356-1_8.
[28] “The LCG Grid log,” Available: http://www.cs.huji.ac.il/labs/parallel/workload/l_lcg/index.html, 2005.