AI-Assisted Adaptive Circuit Breaker Monitoring and Failure Prediction for Distributed Cloud Platforms

Dr Somasundaram Krishnan; Dr Somasundaram Krishnan

doi:10.15662/IJAESIT.2026.0902004

Articles

AI-Assisted Adaptive Circuit Breaker Monitoring and Failure Prediction for Distributed Cloud Platforms

Dr Somasundaram Krishnan

Professor, Department of Computer Science and Engineering, Sri Muthukumaran Institute of Technology, Chennai, India

Abstract

Fast and intelligent fault detection and recovery for cloud systems (distributed and composed of dynamically changing microservices, virtualisation and/or geographically distributed nodes) is a big concern. The next research paper presents a framework for adaptive monitoring and prediction of the failure of circuit breakers in order to increase the resilience of distributed cloud environment. It is not just a framework for collecting real time data for telemetry, it also is there to keep an eye on services, identify anomalies and model failure prediction and adaptively control ‘circuit breakers'. Using machine learning methods these operational metrics such as latency, error-rate, request throughput, CPU/ram load, network delay and dependency behavior of the called services are continuously captured and analysed. The predictive actions layer is able to identify failure trends before they can lead to service outages, while the adaptive decision layer can dynamically adapt the circuit breaker levels to take into account the variability of traffic, past failures or workload trends. This unique architecture will allow for proactively forecasting and automating fault isolation to minimize subsequent cascading fault events and thereby providing a high level of service availability and self-healing to a cloud service. In summary, this work demonstrates the AI potential to disrupt the conventional fixed circuit breaker paradigm, bringing intelligence, context and reliability to this solution. It provides a scalable and efficient way of making a system more fault tolerant, reducing downtime and ensuring reliable performance in today's distributed systems, typically deployed in the cloud

Article Information

Journal	International Journal of Advanced Engineering Science and Information Technology (IJAESIT)
Volume (Issue)	Vol. 9 No. 2 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT)
DOI	https://doi.org/10.15662/IJAESIT.2026.0902004
Pages	421-429
Published	April 20, 2026
Copyright	Creative Commons Attribution 4.0 International License
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Dr Somasundaram Krishnan (2026). AI-Assisted Adaptive Circuit Breaker Monitoring and Failure Prediction for Distributed Cloud Platforms. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 9 No. 2 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 421-429. https://doi.org/10.15662/IJAESIT.2026.0902004

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