Self-Healing Subscription-Based Cloud Infrastructure: A Technical Review
DOI:
https://doi.org/10.22399/ijcesen.5209Keywords:
Cloud Computing, Self-Healing Systems, Service Level Agreements, API Management, Anomaly DetectionAbstract
Enterprise cloud environments struggle with maintaining Service Level Agreements. Configuration errors happen frequently. Security threats emerge constantly. Infrastructure failures disrupt operations without warning. Manual intervention cannot keep pace with these challenges. A novel framework tackles these problems head-on. It uses automated API-level controls combined with machine learning detection. The system has an API Gatekeeper at its core. This component selectively disables specific application programming interfaces when problems occur. The isolation is granular. It prevents damage from spreading while keeping other services running. A Compliance Engine watches resource utilization patterns around the clock. Machine learning algorithms build baseline behavior profiles. They spot deviations that signal potential threats or performance problems. When anomalies show up, the framework toggles off problematic APIs right away. Automated remediation kicks in to fix root causes. No human intervention needed. After remediation succeeds and compliance is verified, the system brings everything back online. It re-enables the disabled APIs. Response times drop dramatically compared to manual processes. The framework keeps SLA compliance consistent through proactive detection and automated containment. Organizations get better service reliability. Operational overhead goes down. Security posture gets stronger. The API toggle mechanism marks real progress in cloud service management. It enables precise control at the function level instead of shutting down entire services.
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