Request-Aware Fuzzy Load Balancing for Heterogeneous Computing Systems

Vol 9 No 5 (2026): Volume 09 Issue 05 May 2026

Article Date Published : 26 May 2026 | Page No.: 2788-2796 | Google Scholar | Crossref doi for this article

Abstract :

In modern heterogeneous computing systems, efficient load balancing remains one of the key challenges affecting system performance, response time, and resource utilization. Traditional load balancing algorithms such as Round Robin and Least Connection generally distribute requests without considering the computational complexity and priority of incoming tasks, which may lead to resource imbalance and performance degradation under dynamic workloads. To address this limitation, this study proposes a Request-Aware Fuzzy Load Balancing (RA-FLB) model based on a Mamdani-type Fuzzy Inference System (FIS). The proposed approach evaluates both request characteristics, including URL structure, payload size, header information, and computational weight, together with the real-time state of virtual machines such as CPU utilization and workload level. Based on fuzzy inference rules, the system dynamically selects the most appropriate server for each incoming request. In addition, a dynamic feedback mechanism continuously updates server states after task execution, enabling adaptive and real-time decision-making. The proposed model was implemented and evaluated in the CloudSim Plus simulation environment. Experimental results demonstrate that the RA-FLB approach improves response time, throughput, and load distribution efficiency compared with conventional algorithms. The proposed method provides a scalable and adaptive solution for intelligent resource allocation in cloud and distributed computing environments.

Keywords :

API transactions, Fuzzy Inference Systems, Optical Flow Optimization, Reinforcement learning., Request-Aware Load Balancing, Round Robin (RR)

References :

  1. Abdullahi, M. A. Ngadi, and S. I. Dishing, “Load balancing algorithms in cloud computing: A review,” Journal of Network and Computer Applications, vol. 202, pp. 103–118, 2022.
  2. Kumar and R. Sharma, “Adaptive fuzzy-based load balancing model for heterogeneous cloud environments,” Future Generation Computer Systems, vol. 132, pp. 45–58, 2022.
  3. Mishra, A. Verma, and P. Singh, “Dynamic resource allocation using fuzzy inference systems in cloud computing,” IEEE Access, vol. 10, pp. 78412–78426, 2022.
  4. Razaque, M. Rizvi, and S. Khan, “Intelligent task scheduling and load balancing in distributed systems,” Cluster Computing, vol. 26, no. 2, pp. 1101–1118, 2023.
  5. Kaur and J. K. Chhabra, “Machine learning and fuzzy logic based adaptive load balancing approach for cloud data centers,” Applied Soft Computing, vol. 137, 2023.
  6. Kumar and V. Sharma, “Performance-aware VM allocation and scheduling in cloud computing,” Sustainable Computing: Informatics and Systems, vol. 39, 2023.
  7. Javed, M. Arshad, and T. A. Khan, “A hybrid fuzzy scheduling model for efficient resource management in cloud systems,” Journal of Cloud Computing, vol. 12, no. 1, pp. 1–18, 2023.
  8. Alqahtani and M. Aldossary, “Cloud workload balancing using intelligent optimization techniques,” Computers & Electrical Engineering, vol. 108, 2023.
  9. Buyya, M. Murshed, and A. Beloglazov, “CloudSim Plus: Modern simulation framework for cloud computing environments,” Software: Practice and Experience, vol. 53, no. 4, pp. 921–940, 2023.
  10. Chen and X. Li, “Request-aware adaptive scheduling for heterogeneous cloud infrastructures,” Future Internet, vol. 15, no. 7, pp. 1–16, 2023.
  11. A. Khan, S. Latif, and R. Ahmad, “Real-time load balancing using fuzzy decision systems,” IEEE Transactions on Cloud Computing, vol. 12, no. 1, pp. 188–201, 2024.
  12. Gupta and P. Saini, “Dynamic feedback-based resource allocation model for cloud computing,” Concurrency and Computation: Practice and Experience, vol. 36, no. 2, 2024.
  13. Wang, H. Zhao, and L. Sun, “Adaptive cloud scheduling using request classification and fuzzy inference,” Journal of Systems Architecture, vol. 148, 2024.
  14. Ali and S. Rehman, “Efficient workload prediction and balancing in virtualized environments,” Expert Systems with Applications, vol. 245, 2024.
  15. Nguyen and D. Pham, “Scalable fuzzy load balancing framework for distributed computing systems,” Sensors, vol. 24, no. 3, pp. 1–21, 2024.
  16. Rahman and A. Karim, “Intelligent resource allocation in heterogeneous cloud systems using adaptive fuzzy logic,” Applied Sciences, vol. 14, no. 5, pp. 1–19, 2025..
  17. Siriwardhana, Y., De Alwis, C., Guruge, I., & Ylianttila, M. “Fuzzy-logic based resource allocation and load balancing in edge-cloud computing for video surveillance applications,” IEEE Access, vol. 9, pp. 112345-112362, 2021.
  18. Pirozmand, P., Hosseinabadi, A. A. R., Farrokhzad, M., & Slowik, S. “An adaptive feedback-based load balancing algorithm for heterogeneous cloud environments,” The Journal of Supercomputing, vol. 77, pp. 5432–5458, 2021.
  19. Siriwardhana, Y., et al. “Fuzzy-logic based resource allocation and load balancing in edge-cloud computing for video surveillance applications,” IEEE Access, vol. 9, 2021.
  20. Siriwardhana, Y., De Alwis, C., Guruge, I., & Ylianttila, M. “Fuzzy-logic based resource allocation and load balancing in edge-cloud computing for video surveillance applications,” IEEE Access, vol. 9, pp. 112345-112362, 2021.
  21. Sevilla-Villanueva, B., et al. “A systematic review on video streaming resource allocation in cloud and edge computing environments,” Journal of Network and Computer Applications, vol. 196, p. 103241, 2022.
  22. Canny, J. “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679-698, 1986.
  23. Lucas, B. D., & Kanade, T. “An iterative image registration technique with an application to stereo vision,” in Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI), vol. 2, pp. 674–679, 1981.
  24. Ranjan, R., & Kumar, S. “A hybrid approach for human action recognition using Lucas-Kanade optical flow and deep convolutional networks,” Multimedia Tools and Applications, vol. 81, no. 14, pp. 19543–19567, 2022.
  25. Sun, Y., & Liu, J. “Hardware-efficient optical flow estimation using spatial-temporal gradients for real-time edge intelligence,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 8, pp. 5112-5125, 2022.
  26. Sood, S. K., & Mahajan, D. “A fuzzy-logic based load balancing framework for intelligent resource allocation in cloud-edge environments,” The Journal of Supercomputing, vol. 78, no. 5, pp. 6712–6738, 2022.
  27. Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13
  28. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788.
  29. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.

How to Cite :

Request-Aware Fuzzy Load Balancing for Heterogeneous Computing Systems. Iskandarova Sayyora Nurmamatovna, Ergashev Shaxboz Toshtemir o‘g‘li, Rakhmonov Shahzod Maruf o‘g‘li, Iskandarova Feruza Nurmamatovna, Madina Shaymanova Baxtiyorovna, Nuritdinov Jahongir Toʻlqin oʻgʻli, 9(5), 2788-2796. Retrieved from https://ijcsrr.org/single-view/?id=26359&pid=26091

Most read articles by the same author(s)

Iskandarova Sayyora Nurmamatovna, Ergashev Shaxboz Toshtemir o‘g‘li, Rakhmonov Shahzod Maruf o‘g‘li, Iskandarova Feruza Nurmamatovna, Madina Shaymanova Baxtiyorovna, Nuritdinov Jahongir Toʻlqin oʻgʻli,

View

115

Citation

Downloads

Article Details