AI-Driven Cloud Resource Optimization for Multi-Cluster Environments
This addresses the problem of operational overhead and inefficiency for cloud platform operators, but it appears incremental as it builds on existing AI and resource management techniques.
The paper tackled the problem of inefficient resource management in multi-cluster cloud environments by proposing an AI-driven framework that dynamically optimizes resource allocation, resulting in improved resource efficiency, faster stabilization during workload fluctuations, and reduced performance variability compared to conventional approaches.
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.