Autonomous Cloud Native Enterprise Systems Using Machine Learning-Based Resource Optimization and Governance
DOI:
https://doi.org/10.15680/mkccct66Keywords:
Autonomous Systems, Cloud-Native Architecture, Machine Learning, Resource Optimization, Cloud Governance, Predictive Analytics, Reinforcement Learning, Enterprise Cloud Systems, Dynamic Workload Management, Cloud ComplianceAbstract
The increasing adoption of cloud-native architectures in enterprises has led to complex resource management and governance challenges. Modern enterprise systems demand high scalability, optimal resource utilization, and dynamic workload management to ensure cost efficiency, reliability, and operational continuity. Traditional static resource allocation methods and manual governance frameworks are insufficient to handle the dynamic and heterogeneous nature of cloud-native infrastructures.
This research proposes a Machine Learning (ML)-based framework for autonomous cloud-native enterprise systems that optimizes resource allocation and enforces governance policies. The framework leverages predictive analytics, reinforcement learning, and anomaly detection to monitor cloud workloads, predict resource demands, and dynamically allocate computing, storage, and network resources. Simultaneously, it integrates governance mechanisms to ensure compliance with internal policies, industry standards, and regulatory requirements.
The methodology involves system architecture modeling, simulation-based performance evaluation, and comparative analysis with conventional cloud management frameworks. Results indicate that ML-driven resource optimization significantly reduces operational costs, enhances system performance, and ensures compliance with governance standards. The proposed autonomous cloud-native framework facilitates intelligent decision-making, reduces human intervention, and improves scalability, reliability, and governance in enterprise cloud environments.
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