Authors : Muvva Venkatesh; Mohammed Taha; Dr. K. Rajitha; Dr. K. Sreekala

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/3n8tdj4y

Scribd : https://tinyurl.com/3mxuxczu

DOI : https://doi.org/10.38124/ijisrt/26May1147

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Cloud computing platforms such as AWS, Microsoft Azure, Google Cloud Platform (GCP), and Cloudflare provide critical infrastructure for modern applications. Despite high availability guarantees, service outages continue to occur due to various factors including infrastructure failures, network disruptions, and configuration errors. These incidents significantly impact service reliability and user experience. This paper proposes a multi-cloud incident analysis framework based on a normalized dataset constructed using a standardized 14-column reliability schema. The framework introduces a COREM (Cloud Outage Risk Evaluation Model) weighted scoring algorithm to quantify and compare the risk associated with incidents across different cloud providers. The model evaluates incidents based on severity, duration, impact, and frequency. The system enables cross-provider comparative analysis and visualization of outage trends, helping to identify reliability patterns and high-risk services. The proposed approach improves transparency in cloud reliability assessment and supports better decision-making for multi-cloud deployment strategies.

Keywords : Cloud Computing, Incident Analysis, Reliability Assessment, Multi-Cloud, COREM Model, Risk Scoring, Outage Analysis Calibration Explainable Artificial Intelligence.

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