AI-Enhanced Linux VMs in Enterprise Cloud Systems
DOI:
https://doi.org/10.63282/3050-9416.ICAIDSCT26-142Keywords:
AI Infrastructure, Cloud Computing, DevOps, Linux Virtualization, Multi-Cloud, SRE, Virtual MachinesAbstract
Cloud-native platforms supporting mission-critical enterprise workloads require infrastructure that is reliable, observable, and adaptive at scale. This paper presents a technical framework for AI-enhanced Linux virtual machine (VM) infrastructure designed to address the operational demands of DevOps, Site Reliability Engineering (SRE), and AI infrastructure teams across public, private, hybrid, and multi-cloud environments. The proposed approach combines low-level Linux systems optimization with AI-driven automation to deliver deterministic compute, memory, and I/O behavior while improving fault tolerance and operational efficiency. Artificial intelligence augments traditional infrastructure management through predictive monitoring, automated anomaly detection, intelligent workload placement, and self-healing remediation workflows, reducing operational toil and improving service-level objectives (SLOs) and mean time to recovery (MTTR). The framework is evaluated through industry case studies involving enterprise platforms such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and mission critical application, demonstrating improved service availability, faster incident response, and consistent performance across cloud domains. The results show that integrating AI-driven tooling with Linux virtualization transforms infrastructure into an active reliability and service optimization layer for modern enterprise cloud operations.
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