Microservices-Based Framework for Predictive Analytics and Real-time Performance Enhancement in Travel Reservation Systems

The paper presents a framework of microservices-based architecture dedicated to enhancing the performance of real-time travel reservation systems using the power of predictive analytics. Traditional monolithic systems are bad at scaling and performing with high loads, causing backup resources to be underutilized along with delays. To overcome the above-stated problems, we adopt a modularization approach in decoupling system components into independent services that can grow or shrink according to demand. Our framework also includes real-time predictive analytics, through machine learning models, that optimize forecasting customer demand, dynamic pricing, as well as system performance. With an experimental evaluation applying the approach, we could show that the framework impacts metrics of performance such as response time, throughput, transaction rate of success, and prediction accuracy compared to their conventional counterparts. Not only does the microservices approach improve scalability and fault tolerance like a usual architecture, but it also brings along timely and accurate predictions, which imply a greater customer satisfaction and efficiency of operation. The integration of real-time analytics would lead to more intelligent decision-making, thereby improving the response of the system along with the reliability it holds. A scalable, efficient framework is offered by such a system to address the modern challenges imposed by any form of travel reservation system while considering other complex, data-driven industries as future applications. Future work will be an investigation of advanced AI models and edge processing to further improve the performance and robustness of the systems employed.