# AI-Driven Cloud Resource Optimization for Multi-Cluster Environments

arXiv: https://arxiv.org/abs/2512.24914 · HTML: https://mlsanity.com/paper/2512.24914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 31 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [A Privacy-Preserving Cloud Architecture for Distributed Machine Learning at Scale](https://mlsanity.com/paper/2512.10341/) (2025)
- [An SLO Driven and Cost-Aware Autoscaling Framework for Kubernetes](https://mlsanity.com/paper/2512.23415/) (2025)
- [Governing Cloud Data Pipelines with Agentic AI](https://mlsanity.com/paper/2512.23737/) (2025)
- [Scalable Cloud-Native Architectures for Intelligent PMU Data Processing](https://mlsanity.com/paper/2512.22231/) (2025)
- [Secure and Governed API Gateway Architectures for Multi-Cluster Cloud Environments](https://mlsanity.com/paper/2512.23774/) (2025)

## Abstract (arXiv)

Modern cloud-native systems increasingly rely on multi-cluster deployments to support scalability, resilience, and geographic distribution. However, existing resource management approaches remain largely reactive and cluster-centric, limiting their ability to optimize system-wide behavior under dynamic workloads. These limitations result in inefficient resource utilization, delayed adaptation, and increased operational overhead across distributed environments. This paper presents an AI-driven framework for adaptive resource optimization in multi-cluster cloud systems. The proposed approach integrates predictive learning, policy-aware decision-making, and continuous feedback to enable proactive and coordinated resource management across clusters. By analyzing cross-cluster telemetry and historical execution patterns, the framework dynamically adjusts resource allocation to balance performance, cost, and reliability objectives. A prototype implementation demonstrates improved resource efficiency, faster stabilization during workload fluctuations, and reduced performance variability compared to conventional reactive approaches. The results highlight the effectiveness of intelligent, self-adaptive infrastructure management as a key enabler for scalable and resilient cloud platforms.