International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 16, No. 5, 8 Oct. 2026
Cover page and Table of Contents: PDF (size: 1420KB)
PSO-Optimized Hybrid Scheduler Integrating RR, HRRN and SRTF for Cloud Task Scheduling
PDF (1420KB), PP.264-283
Views: 0 Downloads: 0
Author(s)
Anand Gautam
1
Narander Kumar
1,*
Index Terms
Task Scheduling, Round Robin, Highest Response Ratio Next, PSO-based Scheduling, Dynamic Time Quantum
Abstract
Nowadays, in cloud computing, task scheduling is a key factor to enhance the performance of the distributed computing environment. As workloads vary dynamically, traditional scheduling algorithms like RR and HRRN have limitations in achieving optimal performance. To overcome these constraints, this paper proposes a PSO-Optimized Hybrid Scheduler that integrates Particle Swarm Optimization (PSO) with RR, HRRN, and Shortest Remaining Time First (SRTF) scheduling approaches. In this integrated approach, the PSO role is to find the optimal task execution order, the RR role is to ensure fair time-sharing among tasks, and the HRRN role is to prioritize processes to dynamically reduce excessive waiting time and prevent starvation. The SRTF provides short-job bias through the inverse remaining time term. The proposed mechanism is evaluated by using AWT and ATAT as key metrics. A Python-based simulation and a data set of six processes are used for validation and implementation, and a mathematical analysis is also presented to ensure correctness. The proposed PSO-optimized Hybrid Scheduler shows lower waiting and turnaround times than classical RR and HRRN scheduling approaches. In comparison with HRRN, the improvements of the proposed mechanism in AWT, ATAT, ART, and AS are 3.6%, 2.3%, 3.6%, and 7.9%, respectively. The findings are that the proposed mechanism provides an effective, balanced solution for resource scheduling in cloud environments.
Cite This Paper
Anand Gautam, Narander Kumar, "PSO-Optimized Hybrid Scheduler Integrating RR, HRRN and SRTF for Cloud Task Scheduling", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.5, pp. 264-283, 2026. DOI:10.5815/ijwmt.2026.05.16
Reference
[1]Karishma, & Kumar, H. (2023). A new hybrid particle swarm optimization algorithm for optimal tasks scheduling in distributed computing system. Intell. Syst. Appl., 18, 200219. DOI: https://doi.org/10.1016/j.iswa.2023.200219
[2]Murad, S. A., Muzahid, A. J. M., Azmi, Z. R. M., Hoque, M. I., & Kowsher, M. (2022). A review on job scheduling technique in cloud computing and priority rule based intelligent framework. Journal of King Saud University-Computer and Information Sciences, 34(6), 2309-2331. DOI: https://doi.org/10.1016/j.jksuci.2022.03.027
[3]Devi, N., Dalal, S., Solanki, K., Dalal, S., Lilhore, U. K., Simaiya, S., & Nuristani, N. (2024). A systematic literature review for load balancing and task scheduling techniques in cloud computing. Artificial Intelligence Review, 57(10), 276. DOI: https://doi.org/10.1007/s10462-024-10925-w
[4]Sanjalawe, Y., Al-E’mari, S., Fraihat, S., & Makhadmeh, S. (2025). AI-driven job scheduling in cloud computing: a comprehensive review. Artificial Intelligence Review, 58(7), 197. DOI: https://doi.org/10.1007/s10462-025-11208-8
[5]Khaleel, M. I., Safran, M., Alfarhood, S., & Gupta, D. (2024). Combinatorial metaheuristic methods to optimize the scheduling of scientific workflows in green DVFS-enabled edge-cloud computing. Alexandria Engineering Journal, 86, 458-470. DOI: https://doi.org/10.1016/j.aej.2023.11.074
[6]Harki, N., Ahmed, A., & Haji, L. (2020). CPU scheduling techniques: A review on novel approaches strategy and performance assessment. Journal of Applied Science and Technology Trends, 1(1), 48-55. DOI: https://doi.org/10.38094/jastt1215
[7]Sana, M. U., & Li, Z. (2021). Efficiency aware scheduling techniques in cloud computing: a descriptive literature review. PeerJ Computer Science, 7, e509. DOI: http://dx.doi.org/10.7717/peerj-cs.509
[8]Potluri, S., Hamad, A. A., Godavarthi, D., & Basa, S. S. (2023). Enhanced Task Scheduling Using Optimised Particle Swarm Optimisation Algorithm in Cloud Computing Environment. ICST Transactions on Scalable Information Systems, 1-5. DOI: https://doi.org/10.4108/eetsis.4042
[9]Rana, N., Jeribi, F., Khan, Z., Alrawagfeh, W., Ben Dhaou, I., Haseebuddin, M., & Uddin, M. (2024). A systematic literature review on contemporary and future trends in virtual machine scheduling techniques in cloud and multi-access computing. Frontiers in Computer Science, 6, 1288552. DOI: https://doi.org/10.3389/fcomp.2024.1288552
[10]Alsaidy, S. A., Abbood, A. D., & Sahib, M. A. (2022). Heuristic initialization of PSO task scheduling algorithm in cloud computing. Journal of King Saud University-Computer and Information Sciences, 34(6), 2370-2382. DOI: https://doi.org/10.1016/j.jksuci.2020.11.002
[11]Fiad, A., Maaza, Z. M., & Bendoukha, H. (2020). Improved version of round robin scheduling algorithm based on analytic model. International Journal of Networked and Distributed Computing, 8(4), 195-202.DOI: https://doi.org/10.2991/ijndc.k.200804.001
[12]Aradhya, S. N., & Priya, R. (2022). Improvised Dynamic Round-Robin Scheduling for Optimum Resource Utilization in Cloud Systems. DOI: https://doi.org/10.21203/rs.3.rs-2063899/v1
[13]Bhapkar, H. R., Chandre, P. R., & Mahalle, P. (2025). Minimizing CPU Utilization for Job Scheduling Problems by the Advanced Round Robin Method: A Pragmatic Perspective. ASEAN Journal on Science and Technology for Development, 42(1), 3. DOI: https://doi.org/10.61931/2224-9028.1610
[14]Sanaj, M. S., & Prathap, P. J. (2020). Nature inspired chaotic squirrel search algorithm (CSSA) for multi objective task scheduling in an IAAS cloud computing atmosphere. Engineering Science and Technology, an International Journal, 23(4), 891-902. DOI: https://doi.org/10.1016/j.jestch.2019.11.002
[15]Prity, F. S. (2025). Nature-Inspired optimization algorithms for enhanced load balancing in cloud computing: A comprehensive review with taxonomy, comparative analysis, and future trends. Swarm and Evolutionary Computation, 97, 102053. DOI: https://doi.org/10.1016/j.swevo.2025.102053
[16]Kathole, A. B., Vhatkar, K., Lonare, S., & Kshirsagar, A. P. (2025). Optimization-based resource scheduling techniques in cloud computing environment: A review of scientific workflows and future directions. Computers and Electrical Engineering, 123, 110080. DOI: https://doi.org/10.1016/j.compeleceng.2025.110080
[17]Pachipala, Y., Sureddy, K. S., Kaitepalli, A. S., Pagadala, N., Nalabothu, S. S., & Iniganti, M. (2024). Optimizing task scheduling in cloud computing: An enhanced shortest job first algorithm. Procedia Computer Science, 233, 604-613. DOI: https://doi.org/10.1016/j.procs.2024.03.250
[18]Menaka, M., & Kumar, K. S. (2024). Supportive particle swarm optimization with time-conscious scheduling (SPSO-TCS) algorithm in cloud computing for optimized load balancing. International Journal of Cognitive Computing in Engineering, 5, 192-198. DOI: https://doi.org/10.1016/j.ijcce.2024.05.002
[19]Younes, A., Elnahary, M. K., Alkinani, M. H., & El-Sayed, H. H. (2022). Task Scheduling Optimization in Cloud Computing by Rao Algorithm. Computers, Materials & Continua, 72(3), 4339-4356. DOI: http://dx.doi.org/10.32604/cmc.2022.022824
[20]Shaheen, A. R., & Kumar, S. S. (2022). Tasks Scheduling in Cloud environment using PSO-BATS. DOI: https://doi.org/10.21203/rs.3.rs-945515/v1
[21]Daid, R., Kumar, Y., Hu, Y. C., & Chen, W. L. (2021). An effective scheduling in data centres for efficient CPU usage and service level agreement fulfilment using machine learning. Connection Science, 33(4), 954-974. DOI: https://doi.org/10.1080/09540091.2021.1926929
[22]Wan, S., & Qi, L. (2021). An Improved Coral Reef Optimization‐Based Scheduling Algorithm for Cloud Computing. Journal of Mathematics, 2021(1), 5532288. DOI: https://doi.org/10.1155/2021/5532288
[23]Parthasaradi, V., Karunamurthy, A., Hussaian Basha, C. H., & Senthilkumar, S. (2024). Efficient Task Scheduling in Cloud Computing: A Multi-objective Strategy Using Horse Herd–Squirrel Search Algorithm. International Transactions on Electrical Energy Systems, 2024(1), 1444493. DOI: https://doi.org/10.1155/2024/1444493
[24]Vijay, R., & Sree, T. R. (2023). Resource scheduling and load balancing algorithms in cloud computing. Procedia computer science, 230, 326-336. DOI: https://doi.org/10.1016/j.procs.2023.12.088
[25]Houssein, E. H., Gad, A. G., Wazery, Y. M., & Suganthan, P. N. (2021). Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm and Evolutionary Computation, 62, 100841. DOI: https://doi.org/10.1016/j.swevo.2021.100841
[26]Shaheen, A. R., & Kumar, S. S. (2023). Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE. Intelligent Automation & Soft Computing, 35(3), 2963-2978. DOI: http://dx.doi.org/10.32604/iasc.2023.025780
[27]Stephen, A., Shanthan, B. H., & Ravindran, D. (2018). Enhanced round Robin algorithm for cloud computing. Int J Sci Res Comput Sci Appl Manag Stud, 7(4), 1-5.
[28]Lal, C., & Sharma, H. (2025). A Workflow Scheduling Using an Efficient Hybrid PSO-MGWO Algorithm in Cloud Computing. SN Computer Science, 6(8), 929. DOI: https://doi.org/10.1007/s42979-025-04416-0