Optimizing Airline Reservation Systems with Edge-Enabled Microservices: A Framework for Real-Time Data Processing and Enhanced User Responsiveness
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Abstract (arXiv)
The growing complexity of the operations of airline reservations requires a smart solution for the adoption of novel approaches to the development of quick, efficient, and adaptive reservation systems. This paper outlines in detail a conceptual framework for the implementation of edge computing microservices in order to address the shortcomings of traditional centralized architectures. Specifically, as edge computing allows for certain activities such as seat inventory checks, booking processes and even confirmation to be done nearer to the user, thus lessening the overall response time and improving the performance of the system. In addition, the framework value should include achieving the high performance of the system such as low latency, high throughput and higher user experience. The major design components include deployed distributed computing microservices orchestrated by Kubernetes, real-time message processing system with Kafka and its elastic scaling. Other operational components include Prometheus and Grafana, which are used to monitor and manage resources, ensuring that all operational processes are optimized. Although this research focuses on a design and theoretical scheming of the framework, its use is foreseen to be more advantageous in facilitating a transform in the provision of services in the airline industry by improving customers' satisfaction, providing infrastructure which is cheap to install and efficiently supporting technology changes such as artificial intelligence and internet of things embedded systems. This research addresses the increasing demand for new technologies with modern well-distributed and real-time-centric systems and also provides a basis for future case implementation and testing. As such, the proposed architecture offers a market-ready, extensible solution to the problems posed by existing airline reservation systems .
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 54 references that are in the map. First posted on arXiv 19 Nov 2024.
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References · 9
- 2019Edge-Computing-Enabled Smart Cities: A Comprehensive Surveycited by 13
- 2024Blockchain-Based Trust and Transparency in Airline Reservation Systems using Microservices Architecturecited by 6
- 2024Optimizing Travel Itineraries with AI Algorithms in a Microservices Architecture: Balancing Cost, Time, Preferences, and Sustainabilitycited by 5
- 2024Enhancing Resilience and Scalability in Travel Booking Systems: A Microservices Approach to Fault Tolerance, Load Balancing, and Service Discoverycited by 4
- 2021Online Deployment Algorithms for Microservice Systems with Complex Dependenciescited by 3
- 2024Novel Architecture for Distributed Travel Data Integration and Service Provision Using Microservicescited by 3
- 2024Leveraging Microservices Architecture for Dynamic Pricing in the Travel Industry: Algorithms, Scalability, and Impact on Revenue and Customer Satisfactioncited by 2
- 2021Market-Oriented Online Bi-Objective Service Scheduling for Pleasingly Parallel Jobs with Variable Resources in Cloud Environmentscited by 1
- 2024A Next-Generation Approach to Airline Reservations: Integrating Cloud Microservices with AI and Blockchain for Enhanced Operational Performancecited by 1
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