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arXiv · 2410.19701

Enhancing Resilience and Scalability in Travel Booking Systems: A Microservices Approach to Fault Tolerance, Load Balancing, and Service Discovery

Biman Barua·M. Shamim Kaiser
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Abstract

This paper investigates the inclusion of microservices architecture in the development of scalable and reliable airline reservation systems. Most of the traditional reservation systems are very rigid and centralized which makes them prone to bottlenecks and a single point of failure. As such, systems do not meet the requirements of modern airlines which are dynamic. Microservices offer better resiliency and scalability because the services do not depend on one another and can be deployed independently. The approach is grounded on the Circuit Breaker Pattern to maintain fault tolerance while consuming foreign resources such as flight APIs and payment systems. This avoided the failure propagation to the systems by 60% enabling the systems to function under external failures. Traffic rerouting also bolstered this with a guarantee of above 99.95% uptime in systems where high availability was demanded. To address this, load balancing was used, particularly the Round-Robin method which managed to enhance performance by 35% through the equal distribution of user requests among the service instances. Health checks, as well as monitoring in real-time, helped as well with failure management as they helped to contain failures before the users of the system were affected. The results suggest that the use of microservices led to a 40% increase in system scalability, a 50% decrease in downtime and a support for 30% more concurrent users than the use of monolithic architectures. These findings affirm the capability of microservices in the development of robust and flexible airline ticket booking systems that are responsive to change and recover from external system unavailability.

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Biman Barua, M. Shamim Kaiser. 2024-10-25. Enhancing Resilience and Scalability in Travel Booking Systems: A Microservices Approach to Fault Tolerance, Load Balancing, and Service Discovery. https://arxiv.org/abs/2410.19701

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