A Review of AI and Machine Learning Solutions for Fault Detection and Self-Healing in Cloud Services
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I3P107Keywords:
Artificial Intelligence, Machine Learning, Fault Detection, Self-Healing, Cloud ServicesAbstract
The reliability and availability of cloud services are thus critical issues as the usage of cloud computing as a fundamental aspect of present-day digital infrastructure continues to grow. As cloud environments become more complex and larger, the utility of the traditional fault detection schemes and approaches is inadequate, thus providing possibilities of system crashes, poor performance, and interruption of services. In this paper, researching the use of Artificial Intelligence (AI) and Machine Learning (ML) in detecting faults and self-healing in cloud services is performed. It highlights how the traditional rule-based monitoring is moving towards AI-based solutions that utilize large operational datasets in order to find anomalies and perform predictive maintenance. It classifies many types of AI / ML models, such as supervised models, unsupervised models and deep learning models, and explains their success or effectiveness in detecting faults and automating recovery operations. Moreover, it also considers the issues and prospects of including AI/ML in fault management of the cloud environment with the final goal of achieving system resilience and operational effectiveness
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