An application-oriented scheduler

TL;DR

This paper introduces an application-oriented scheduler for multi-agent radio resource access, combining application parameters and environment knowledge to optimize performance with a low-complexity solver, outperforming standard schedulers.

Contribution

It presents a novel scheduling approach that integrates application-level resilience and radio environment data into an optimization framework with an efficient solver.

Findings

Outperforms state-of-the-art schedulers in simulations

Offers flexible performance tuning through application parameters

Demonstrates effectiveness in multi-agent radio access scenarios

Abstract

We consider a multi-agent system where agents compete for the access to the radio resource. By combining some application-level parameters, such as the resilience, with a knowledge of the radio environment, we propose a new way of modeling the scheduling problem as an optimization problem. We design accordingly a low-complexity solver. The performance are compared with state-of-the-art schedulers via simulations. The numerical results show that this application-oriented scheduler performs better than standard schedulers. As a result, it offers more space for the selection of the application-level parameters to reach any arbitrary performance.