Dynamic Computational Resource Allocation for Ensuring Stability of Remote Edge-based Controlled Multi-agent Systems

This article presents a novel edge-based architecture to dynamically allocate resources to edge-offloaded controllers for multi-agent systems. The proposed controllers are designed to generate collision-free trajectories to track the desired reference positions. The computational complexity of the controllers’ problem is estimated by a second-order polynomial regression model, while the Least Squares (LS) minimization technique is employed for the coefficients’ estimation. The covariance matrix plays an essential role in assessing the confidence in the parameter estimates and in investigating correlations among parameters. Through this curve fitting process, we can dynamically estimate the complexity of the controllers’ problem as conditions change, enabling effective and responsive resource allocation. Furthermore, a novel control law is designed to control the dynamic resource allocation, based on the measured communication and processing time delays. This approach allows us to control the controllers’ response time, thereby ensuring the closed-loop system’s stability. The overall architecture is enabled through a Kubernetes (k8s) cluster and is experimentally evaluated.