Intelligent AI Frameworks for Enterprise Cloud Infrastructure Optimization and Autonomous Workload Management
DOI:
https://doi.org/10.64235/2s5zg938Keywords:
Artificial Intelligence, Machine Learning, AI Agents, Cloud Infrastructure, Cloud Optimization, Autonomous Workload Management, Enterprise Cloud Computing, Resource Allocation, Predictive Analytics, Reinforcement Learning, Kubernetes, Cloud-Native Computing, Workload Scheduling, Infrastructure Automation, Intelligent Cloud ManagementAbstract
Enterprise cloud environments have become increasingly complex due to the rapid adoption of containers, microservices,
serverless computing, distributed databases, and multi-cloud architectures. Conventional infrastructure management approaches
depend heavily on static configurations, predefined scaling rules, and manual intervention, which can result in
inefficient resource utilization, excessive operational costs, performance degradation, and delayed responses to workload
changes. Intelligent artificial intelligence frameworks offer an opportunity to transform cloud infrastructure management
from reactive automation into predictive, adaptive, and autonomous optimization. This research proposes an intelligent
AI framework for enterprise cloud infrastructure optimization and autonomous workload management. The framework
integrates machine learning, reinforcement learning, predictive analytics, AI agents, cloud-native orchestration, observability,
and policy-based automation to continuously monitor infrastructure conditions and make context-aware resource
management decisions. The proposed methodology combines systematic literature analysis, framework design, prototype
implementation, controlled workload experimentation, and quantitative evaluation. The framework is designed to
predict workload demand, optimize compute and storage resources, perform intelligent workload placement, detect
anomalies, balance performance and cost, and autonomously respond to infrastructure changes. Evaluation focuses on
resource utilization, infrastructure cost, workload performance, energy efficiency, scaling accuracy, service-level objective
compliance, recovery time, and autonomous decision accuracy. The research aims to establish a practical and measurable
approach for deploying intelligent cloud infrastructure optimization while maintaining enterprise security, governance,
reliability, and human oversight.
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