Cloud-Based Scheduling Mechanism for Scalable and Resource-Efficient Centralized Controllers

TL;DR

This paper introduces a Kubernetes-based cloud scheduling system to improve scalability and resource efficiency of centralized control in multi-robot systems, demonstrating effective offloading of computational tasks in real-time environments.

Contribution

It presents a novel cloud-based scheduling mechanism for CNMPCs that enhances scalability and resource management in large-scale multi-agent robotic systems.

Findings

Effective offloading of CNMPC computations in real-time cloud environments

Improved scalability with changing numbers of robots

Demonstrated performance benefits in experimental scenarios

Abstract

This paper proposes a novel approach to address the challenges of deploying complex robotic software in large-scale systems, i.e., Centralized Nonlinear Model Predictive Controllers (CNMPCs) for multi-agent systems. The proposed approach is based on a Kubernetes-based scheduling mechanism designed to monitor and optimize the operation of CNMPCs, while addressing the scalability limitation of centralized control schemes. By leveraging a cluster in a real-time cloud environment, the proposed mechanism effectively offloads the computational burden of CNMPCs. Through experiments, we have demonstrated the effectiveness and performance of our system, especially in scenarios where the number of robots is subject to change. Our work contributes to the advancement of cloud-based control strategies and lays the foundation for enhanced performance in cloud-controlled robotic systems.