Cloud-Enabled Model-Driven Approaches for Circuit Breaker Pattern Implementation with Fault Tolerance
DOI:
https://doi.org/10.15662/IJEETR.2026.0802014Keywords:
Cloud computing, model-driven development, circuit breaker pattern, fault tolerance, microservices, cloud-based architecture, scalabilityAbstract
The article under research is entitled Cloud-Enabled Model-Driven Approaches to Circuit Breaker Pattern Implementation with Fault Tolerance: the article is an innovative framework that uses cloud computing and model-driven development methods to increase the reliability and fault tolerance of the circuit breaker pattern to software systems. The circuit breaker pattern is crucial in the microservices designs, where it assists in avoiding the failure of the system by isolating faults in the services. Conventional uses of the circuit breaker pattern can be difficult in terms of scalability, flexibility and fault tolerance when the pattern is used in cloud-based application. In this paper, a cloud-supported model-driven solution has been suggested to simplify the design and implementation of the circuit breaker pattern to be more flexible to the changing cloud infrastructures. The model-driven development is incorporated in the framework in order to automatize the development of circuit breaker settings such that fault tolerance measures are implemented accordingly depending on real-time system states. The paper also explains how the same method can be generalized to a range of cloud-based architectures to offer solutions to various cases of faults, including the occurrence of service failures, network failures, or even unavailability of resources. The proposed model provides the high availability and resilience of microservices by utilizing cloud-based resources and can scale the mechanisms of fault tolerance depending on the demand. The article can prove the effectiveness of the suggested framework in improving the fault tolerance of distributed systems, which is the scalable and reliable solution to the current cloud applications, based on the experimental findings and case studies.
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