Comparative modeling of computational load distribution heuristics in a hybrid cloud-fog architecture for IoT systems

This paper presents a comparative analysis of heuristics for workload distribution based on a hybrid cloud-fog architecture used for processing Internet of Things traffic. Such an architecture is optimal for applications requiring high performance and operating in real time, taking into account constraints on latency, power consumption and the load on computing nodes. Comparative modelling is carried out using a stochastic multi-criteria mathematical model of computational load distribution between fog and cloud resources. Constraints relating to capacity, latency, reliability and load balancing are applied to the model. Using iFogSim, a typical IoT scenario was modelled with variations in task flow intensity and a comparison of two distribution policies – a baseline and an optimised one. A dynamic analysis was carried out with the load varying according to the intensity of request arrivals. The results show that the optimised strategy reduces average latency, energy consumption, the proportion of SLA violations and the degree of fog node overload, ensuring more stable system operation as the load increases. With this approach, fog and cloud resources are utilised more efficiently, thereby improving the efficiency of IoT data processing. The practical significance of this work lies in the potential to apply the simulation results when developing intelligent load management algorithms for industrial IoT systems, smart cities, telemedicine and video surveillance systems.

1. Utegenov N. Internet of Things (IoT) and information systems. Universum: Technical Sciences. 2023;(7-1):30–34. (In Russ.).

2. Alsweity M. Deep Learning Approaches for Traffic Prediction Forecasting in Multi-Level Cloud Architecture for Autonomous Vehicle Services. Proceedings of Telecommunication Universities. 2022;8(4):89–99. (In Russ.). https://doi.org/10.31854/1813-324X-2022-8-4-89-99

3. Glushak E.V., Mikhailova P.D. Improving traffic quality of service in hybrid networks with cloud and fog layers. Modeling, Optimization and Information Technology. 2026;14(3). (In Russ.). https://doi.org/10.26102/2310-6018/2026.54.3.001

4. Melnik E.V., Ivanov D.Ya., Orda-Zhigulina M.V., et al. Application of fog computing for monitoring and forecasting system of hazardous natural phenomena. News of the Tula State University. Technical Sciences. 2019;(2):300–311. (In Russ.).

5. Volkov A.N. Routing Task in Dynamic Fog Computing Network. Proceedings of Telecommunication Universities. 2024;10(4):27–37. (In Russ.). https://doi.org/10.31854/1813-324X-2024-10-4-27-37

6. Glushak E.V., Klyuev D.S. Application of cloud and fog computing for data processing on board aircraft. Trudy MAI. 2026;(146):18. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=187458

7. Glushak E.V. Analysis of priority traffic quality management in hybrid communication networks with cloud and fog technologies. Dynamics of Complex Systems – XXI Century. 2026;20(2):38–45. (In Russ.). https://doi.org/10.18127/j19997493-202602-04

8. Volkov A.N. Dynamic Fog Computing Towards Green ICT. Proceedings of Telecommunication Universities. 2024;10(3):24–34. (In Russ.). https://doi.org/10.31854/1813-324X-2024-10-3-24-34

9. Kupriyanov D.O. Mathematical modeling of requests flow to cloud compute cluster. T-Comm. 2020;14(10):39–44. (In Russ.). https://doi.org/10.36724/2072-8735-2020-14-10-39-44

10. Dang V.T., Volkov А.N. Analyzing the impact of green it on distributed fog computing: opportunities and challenges. The Scientific and Technical Conference of the St. Petersburg STS RES named after A.S. Popov, Dedicated to Radio Day. 2025;(1):171–174. (In Russ.).