Application of the theory of automatic control in communication networks with cloud-fog architecture

The article is devoted to the application of methods of the theory of automatic control systems for traffic and resource management in a hybrid cloud fog IoT network in order to improve the quality of service. A network model is proposed in the form of a system of discrete equations of state describing the dynamics of queues at fog nodes, channel loading and interaction with the cloud layer, and an LQR criterion is introduced that simultaneously takes into account latency and resource costs. Based on the solution of the discrete Riccati equation, a distributed controller is synthesized, implemented on fog nodes and in the cloud, which makes it possible to adaptively redistribute bandwidth, priorities and routing parameters, taking into account the current load and traffic disturbances. To evaluate the effectiveness, the developed LQR controller is integrated into the OMNeT++ simulator. An IoT topology consisting of several fog nodes and a cloud server with MQTT traffic and various load scenarios (base, peak and surge modes) is modeled. The simulation results show that, compared with the non controlled mode and the fixed LQR option, adaptive LQR significantly reduces average latency and jitter, reduces packet loss and queue occupancy with a moderate increase in CPU usage, which confirms the applicability of the proposed approach to optimize the quality of service in modern cloud-fog telecommunications systems.

1. Glushak E.V. Research and development of a model for automatic traffic flow management in cloud-fog infrastructures. Telecommunications. 2026;(2):2–7. (In Russ.). https://doi.org/10.34832/ELSV.2026.76.2.001

2. Titova I.V., Diakonov D.Yu. Fog Computing – the foundation for distributed digital project evolution and security in business. Modeling, Optimization and Information Technology. 2025;13(4). (In Russ.). https://doi.org/10.26102/2310-6018/2025.51.4.036

3. Bonomi F., Milito R.A., Zhu J., Addepalli S. Fog Computing and Its Role in the Internet of Things. In: MCC'12: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, 17 August 2012, Helsinki, Finland. New York: ACM; 2012. P. 13–16. https://doi.org/10.1145/2342509.2342513

4. Mahmud R., Kotagiri R., Buyya R. Fog Computing: A Taxonomy, Survey and Future Directions. In: Internet of Everything: Algorithms, Methodologies, Technologies and Perspectives. Singapore: Springer; 2018. P. 103–130. https://doi.org/10.1007/978-981-10-5861-5_5

5. Vorobyov S.P. Mathematical model of optimization of the network infrastructure of a distributed enterprise system on a cloud, misty and edge technologies. Modeling, Optimization and Information Technology. 2019;7(3). (In Russ.). https://doi.org/10.26102/2310-6018/2019.26.3.003

6. Klimenko A.B. A resource-saving method of distributed computation planning in fog-computing environment. Modeling, Optimization and Information Technology. 2022;10(3). (In Russ.). https://doi.org/10.26102/2310-6018/2022.38.3.019

7. Bhatia A., Kumar A., Jain A., et al. Networked control system with MANET communication and AODV routing. Heliyon. 2022;8(11). https://doi.org/10.1016/j.heliyon.2022.e11678

8. Taghizad-Tavana K., Ghanbari-Ghalehjoughi M., Razzaghi-Asl N., Nojavan S., Alizadeh A. An Overview of the Architecture of Home Energy Management System as Microgrids, Automation Systems, Communication Protocols, Security, and Cyber Challenges. Sustainability. 2022;14(23). https://doi.org/10.3390/su142315938

9. Liu Ch., Ke L. Cloud assisted Internet of things intelligent transportation system and the traffic control system in the smart city. Journal of Control and Decision. 2022;10(3):1–14. https://doi.org/10.1080/23307706.2021.2024460

10. Lilhore U.K., Imoize A.L., Li Ch.-T., et al. Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities. Sensors. 2022;22(8). https://doi.org/10.3390/s22082908

11. Ramadass R., Venumula Sh., Shankar T.A.S., Syed K. Application Reliable Traffic Control Method for Efficient Data Management in Wireless-aided Computer Applications. International Innovative Research Journal of Engineering and Technology (IIRJET). 2023;8(3):1–8. https://doi.org/10.32595/iirjet.org/v8i3.2023.168

12. 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

13. Tam P., Song I., Kang S., Ros S., Kim S. Graph Neural Networks for Intelligent Modelling in Network Management and Orchestration: A Survey on Communications. Electronics. 2022;11(20). https://doi.org/10.3390/electronics11203371

14. Kim D.P. Theory of Automatic Control. Vol. 1. Linear Systems. Moscow: Fizmatlit; 2010. 310 p. (In Russ.).