Predictive Resilience: Safeguarding Multi-Cloud Infrastructure with Machine Learning

The explosion of enterprises adopting many cloud functionalities has created problems associated with the management of these disparate systems. To meet this challenge, enterprises will begin to move from old ways of executing reactive management to newer, more forward-looking methodologies. This paper will provide an overview of how incorporating predictive analytics and AI can enhance automation in managing multi-cloud environments through an innovative conceptual model that uses machine learning (ML) to improve real-time visibility, anomaly detection and automated remediation to improve operational efficiency and resiliency. Further, this paper will identify measurable performance indicators such as decreased mean-time-to-resolution (MTTR) and fewer service-level agreement (SLA) violations after implementing this model. Challenges in managing multi-cloud environments, including normalizing data between providers, model drift, and issues with integration will also be addressed, as well as potential solutions such as federated learning and autonomous IT operations, to facilitate better governance of multi-cloud environments.

Multi-cloud Environment, Predictive Analytics, Machine Learning, AIOps, Mean-Time-to-Resolution (MTTR), Service-Level Agreement (SLA), Anomaly Detection.

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