AI-Driven Cloud Resource Optimization for Multi-Cluster Environments
Vinoth Punniyamoorthy Akash Kumar Agarwal Bikesh Kumar Abhirup Mazumder Kabilan Kannan Sumit Saha
作者信息
Abstract
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.引用本文复制引用
Vinoth Punniyamoorthy,Akash Kumar Agarwal,Bikesh Kumar,Abhirup Mazumder,Kabilan Kannan,Sumit Saha.AI-Driven Cloud Resource Optimization for Multi-Cluster Environments[EB/OL].(2025-12-31)[2026-01-16].https://arxiv.org/abs/2512.24914.学科分类
计算技术、计算机技术
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