The rapid growth of the travel industry has increased the need for real-time optimization in reservation systems that could take care of huge data and transaction volumes. This study proposes a hybrid framework that ut folds an Artificial Intelligence and a Microservices approach for the performance optimization of the system. The AI algorithms forecast demand patterns, optimize the allocation of resources, and enhance decision-making driven by Microservices architecture, hence decentralizing system components for scalability, fault tolerance, and reduced downtime. The model provided focuses on major problems associated with the travel reservation systems such as latency of systems, load balancing and data consistency. It endows the systems with predictive models based on AI improved ability to forecast user demands. Microservices would also take care of different scales during uneven traffic patterns. Hence, both aspects ensure better handling of peak loads and spikes while minimizing delays and ensuring high service quality. A comparison was made between traditional reservation models, which are monolithic and the new model of AI-Microservices. Comparatively, the analysis results state that there is a drastic improvement in processing times where the system uptime and resource utilization proved the capability of AI and the microservices in transforming the travel industry in terms of reservation. This research work focused on AI and Microservices towards real-time optimization, providing critical insight into how to move forward with practical recommendations for upgrading travel reservation systems with this technology.

翻译：随着旅游业的快速发展，对能够处理海量数据和交易量的预订系统进行实时优化的需求日益增长。本研究提出一种混合框架，融合人工智能与微服务方法以实现系统性能优化。人工智能算法预测需求模式、优化资源分配，并借助微服务架构增强决策能力，从而通过系统组件的去中心化实现可扩展性、容错性和更低停机时间。该模型重点关注旅行预订系统面临的主要问题，如系统延迟、负载均衡与数据一致性。它赋予系统基于人工智能的预测模型，提升预测用户需求的能力。微服务架构还能应对不均匀流量模式下的不同规模需求。因此，这两个方面共同确保系统能更好地处理峰值负载与突发流量，同时最大限度减少延迟并保障高服务质量。本研究对传统的单体式预订模型与新型人工智能-微服务模型进行了对比分析。结果表明，新模型在处理时间、系统正常运行时间和资源利用率方面均有显著提升，证明了人工智能与微服务技术在变革旅游业预订模式方面的潜力。本研究聚焦于人工智能与微服务在实时优化中的应用，为旅行预订系统的技术升级提供了关键见解与实践建议。