Optimizing CI/CD Pipelines in Cloud Foundry Using Machine Learning for Predictive Failure Detection
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Abstract
The rapid evolution of cloud-native application development has necessitated increasingly sophisticated approaches to continuous integration and continuous delivery (CI/CD) pipeline management, particularly within Platform-as-a-Service (PaaS) environments such as Cloud Foundry. Traditional CI/CD pipelines, while effective in automating build, test, and deployment processes, remain predominantly reactive in nature — addressing failures only after they manifest, thereby incurring significant costs in terms of deployment downtime, developer productivity loss, and degraded service reliability. This research proposes a novel framework that integrates machine learning (ML) techniques into Cloud Foundry-based CI/CD pipelines to enable predictive failure detection, transforming pipeline management from a reactive to a proactive paradigm. By continuously collecting and analyzing high-dimensional telemetry data — encompassing build logs, test execution metrics, resource utilization patterns, deployment histories, and inter-service dependency graphs — the proposed system trains supervised and unsupervised ML models, including gradient boosting classifiers, Long Short-Term Memory (LSTM) networks, and isolation forest algorithms, to identify latent failure signatures well before critical pipeline stages are reached. The framework leverages Cloud Foundry's native APIs, service broker architecture, and container orchestration capabilities to seamlessly embed predictive intelligence into existing DevOps workflows without disrupting established development practices. Experimental evaluations conducted on real-world Cloud Foundry deployments demonstrate that the proposed approach achieves a failure prediction accuracy of up to 91.4%, reduces mean time to detection (MTTD) by approximately 63%, and decreases overall pipeline failure rates by 47% compared to conventional rule-based monitoring systems
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