DESIGN OF A CONGESTION CONTROL SYSTEM FOR MULTI-USER TELECOMMUNICATION NETWORK. (A CASE STUDY OF AIRTEL NETWORK)

The study examines the design of a congestion control system for multi-user telecommunication network. A case study of Airtel Nigeria network. Rapid telecommunication network expansion has raised network traffic and user demand, which frequently causes congestion and lowers service quality, especially for multi-user systems. Using Airtel Nigeria as a case study, this paper describes the architecture and deployment of a congestion control system for a multi-user telecom network. JavaScript and HTML were used in the system's development with the goals of effectively managing network traffic, minimizing congestion, and enhancing user experience overall. The first part of the study looks at the difficulties caused by network congestion in multi-user settings, particularly in areas with high population densities where telecommunications companies, like Airtel, deal with heavy traffic loads. These difficulties are exacerbated by elements including a growing subscriber population, data-intensive apps, and constrained network infrastructure. The study suggests a congestion control system that uses load balancing, traffic prioritization, and real-time monitoring to reduce congestion during periods of high demand. While HTML was used to build a user-friendly interface for network operators, JavaScript was used in the system's design to handle data traffic and user interactions dynamically. The main features of the system are real-time network traffic monitoring, traffic shaping, and more equitable network load distribution among the resources. Furthermore, priority queuing algorithms were incorporated to guarantee high-quality service delivery even in times of congestion by giving phone conversations and video streaming priority over other important network services. However, to assess the system's performance, a multi-user environment was simulated on the Airtel network during the installation phase. The outcomes showed a notable decrease in network congestion, which enhanced data transfer rates and reduced the number of lost connections. The system's real-time alerts on congestion status and traffic control features made it easy for network operators to utilize. Test results showed a significant improvement in the user experience, particularly during moments of high traffic. With possible applications in other multi-user networks, this work provides telecom service providers with a scalable answer to their network congestion problems. JavaScript and HTML were used to create a simple, yet efficient, congestion control framework that offered room for improvement. In order to improve system scalability and adaptability in bigger networks, future research should examine the integration of machine learning algorithms for predictive congestion management and the use of more sophisticated programming frameworks.