The exponential growth of Internet traffic generated by a plethora of interconnected apps poses a size challenge, making effective management of incoming requests by a single server difficult, even for the most reputable businesses. To ensure uninterrupted service delivery, IT teams are turning to the deployment of many servers operating inside a distributed system framework. Charge balancing appears to be the best strategy for capitalizing on increasing data traffic, with the dual goal of distributing computation costs over several servers and improving overall infrastructure performance. In order to achieve this goal, a range of solutions, including specialized hardware, dedicated software, or a combination of the two, may be envisaged. The combined use of keepalived with HAProxy has shown a notable reduction in recovery time following a server panel, minimizing stop time to only one second. Furthermore, our investigation reveals that in low-traffic scenarios, the Round Robin algorithm performs better than HAProxy and keepalived, but in high-traffic scenarios, the source IP technique leads. This idea emphasizes how wise it is to evaluate three algorithms and select the best one based on the traffic’s fluctuating bit rate.

This paper presents a novel approach to improving urban road traffic control using artificial intelligence (AI) for dynamic traffic light management. We begin by describing the current context of urban traffic management and the challenges facing traffic light infrastructures. We then explain how AI can be integrated into this context for more effective regulation. We have chosen to use a basic model based on convolutional neural networks (CNN) to model road traffic in real time. This model collects real-time data from traffic cameras and other sensors, pre-processes it and then analyses it to make intelligent decisions about traffic light control. By using historical data and adapting to changing conditions, our model has been able to reduce waiting times at intersections, minimise congestion and improve traffic flow. This research paves the way for more intelligent and adaptive traffic management in urban environments. The practical implications of our approach include more efficient urban mobility, reduced greenhouse gas emissions and improved road safety. Future prospects lie in the continued optimisation of the AI model and its integration with other intelligent transport systems, contributing to more sustainable and liveable cities.