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AI-Driven Cloud Resource Optimization for Multi-Cluster Environments

Vinoth Punniyamoorthy, Akash Kumar Agarwal, Bikesh Kumar, Abhirup Mazumder, Kabilan Kannan, Sumit Saha

TL;DR

The paper addresses cross-cluster resource optimization in multi-cluster cloud environments, where traditional reactive, cluster-centric management falls short under dynamic workloads. It proposes an AI-driven framework that fuses predictive learning, policy-aware decision-making, and continuous feedback within a unified architecture to enable proactive, coordinated resource management across clusters. Empirical evaluation in geographically distributed Kubernetes deployments shows improvements in resource utilization, stability, and responsiveness compared to reactive baselines, demonstrating the approach's practical benefits. By leveraging global observability and closed-loop control, the work advances scalable, cost-efficient, and reliable cloud platforms capable of adapting to evolving workloads and policies.

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.

AI-Driven Cloud Resource Optimization for Multi-Cluster Environments

TL;DR

The paper addresses cross-cluster resource optimization in multi-cluster cloud environments, where traditional reactive, cluster-centric management falls short under dynamic workloads. It proposes an AI-driven framework that fuses predictive learning, policy-aware decision-making, and continuous feedback within a unified architecture to enable proactive, coordinated resource management across clusters. Empirical evaluation in geographically distributed Kubernetes deployments shows improvements in resource utilization, stability, and responsiveness compared to reactive baselines, demonstrating the approach's practical benefits. By leveraging global observability and closed-loop control, the work advances scalable, cost-efficient, and reliable cloud platforms capable of adapting to evolving workloads and policies.

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.
Paper Structure (17 sections, 2 figures, 1 table, 1 algorithm)

This paper contains 17 sections, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Related Work
  4. System Architecture
  5. Telemetry and Observation Layer
  6. Intelligence and Learning Layer
  7. Policy and Decision Layer
  8. Execution and Feedback Layer
  9. System Model
  10. AI-Driven Optimization Workflow
  11. Experimental Evaluation
  12. Experimental Setup
  13. Evaluation Metrics
  14. Results and Observations
  15. Discussion
  16. ...and 2 more sections

Figures (2)

  • Figure 1: AI-driven multi-cluster resource optimization architecture.
  • Figure 2: Convergence behavior of reactive and AI-driven optimization approaches over successive iterations.