NL-CPS: Reinforcement Learning-Based Kubernetes Control Plane Placement in Multi-Region Clusters

2026-04-09

AI Summary

This study addresses the lack of intelligent deployment strategies for Kubernetes control plane nodes in multi-region heterogeneous cloud-edge environments that jointly consider resource capacity and network topology, a gap leading to degraded performance and insufficient resilience. To tackle this challenge, the work proposes a data-driven, adaptive node placement approach by introducing, for the first time, a Neural Contextual Bandit reinforcement learning framework into control plane deployment. The method dynamically optimizes node placement by integrating infrastructure resource metrics with network characteristics. Experimental results demonstrate that the proposed approach significantly outperforms existing baselines across diverse geographical distributions and cluster configurations, effectively enhancing both overall cluster performance and robustness.

Abstract

The placement of Kubernetes control-plane nodes is critical to ensuring cluster reliability, scalability, and performance, and therefore represents a significant deployment challenge in heterogeneous, multi-region environments. Existing initialisation procedures typically select control-plane hosts arbitrarily, without considering node resource capacity or network topology, often leading to suboptimal cluster performance and reduced resilience. Given Kubernetes's status as the de facto standard for container orchestration, there is a need to rigorously evaluate how control-plane node placement influences the overall performance of the cluster operating across multiple regions. This paper advances this goal by introducing an intelligent methodology for selecting control-plane node placement across dynamically selected Cloud-Edge resources spanning multiple regions, as part of an automated orchestration system. More specifically, we propose a reinforcement learning framework based on neural contextual bandits that observes operational performance and learns optimal control-plane placement policies from infrastructure characteristics. Experimental evaluation across several geographically distributed regions and multiple cluster configurations demonstrates substantial performance improvements over several baseline approaches.

Problem

• Kubernetes
• control-plane placement
• multi-region clusters
• cluster performance
• resilience

Innovation

• reinforcement learning
• Kubernetes control plane placement
• neural contextual bandits
• multi-region clusters
• automated orchestration