Trends and Challenges in AI-Driven Microservices for Cloud-Based Airline Reservation Systems: A Review

The rapid advances in Artificial Intelligence (AI), microservices architecture, and cloud computing highly impact airlines in optimizing reservation systems. This review paper attempts to analyze the ongoing trend, challenges, and future opportunities of AI-enabled microservices toward cloud-based airline reservation systems. AI with machine learning and deep learning supports dynamic pricing, personalized services, and better demand forecasting, transforming booking, pricing, and customer experience. While microservices architecture offers scalability and flexibility, in conjunction with cloud computing, it optimizes airline demands and system performance. At the same time, challenges such as data privacy and security, scalability, interoperability, and costs are still present with adopting cloud-based microservices. Case studies are provided in this paper, as well as a thorough exposition of future directions of AI in airline reservation systems, such as the application of emerging AI techniques like reinforcement learning, evolution toward micro service architecture, and the influence of new cloud computing models such as edge and hybrid clouds. In addition, the work points out research gaps in areas such as real-time predictive analytics and security measures. Findings indicate a transforming potential of service on airline reservation systems for AI-facilitated micro services and cloud computing and provide possible suggestions for overcoming the current challenges to harness great efficiency from them.