Anticipatory Autonomic Management of Poly-Cloud Environments: A Machine Intelligence Paradigm

This work introduces a new type of machine intelligence aiming to improve resilience in complex multi-cloud systems. With the help of advanced algorithms like anomaly detection and trend forecasting, this technique enables it to process telemetry from various clouds, to detect when an infrastructure failure will start occurring and when a resource will run out, before services are affected. A graduated automation remediation engine integrates with cloud orchestration tools. Dynamic and appropriate response actions are enabled to dramatically reduce the time to resolve incidents and service outages compared to reactive approaches. This framework can enhance smart budgeting via scaling predictions and balancing workloads. It has been tested on different degradation scenarios. This presents a work that offers unified resilience governance as a remedy for the challenges of cross-provider visibility and agile data functioning. Moreover, it reduces service reliability and operational cost in distributed cloud environments.