Distributed Fair Scheduling for Information Exchange in Multi-Agent Systems

TL;DR

This paper introduces a distributed scheduling algorithm for multi-agent systems that ensures fair and efficient information exchange by dynamically balancing fairness and throughput in shared communication media.

Contribution

It proposes a novel queueing theoretic, distributed fair scheduling algorithm that guarantees bounded disparity in normalized shares among agents, improving short-term fairness and throughput.

Findings

The algorithm guarantees an upper bound on share disparity.

It outperforms existing methods in short-term fairness.

It maintains high throughput while ensuring fairness.

Abstract

Information exchange is a crucial component of many real-world multi-agent systems. However, the communication between the agents involves two major challenges: the limited bandwidth, and the shared communication medium between the agents, which restricts the number of agents that can simultaneously exchange information. While both of these issues need to be addressed in practice, the impact of the latter problem on the performance of the multi-agent systems has often been neglected. This becomes even more important when the agents' information or observations have different importance, in which case the agents require different priorities for accessing the medium and sharing their information. Representing the agents' priorities by fairness weights and normalizing each agent's share by the assigned fairness weight, the goal can be expressed as equalizing the agents' normalized shares of the communication medium. To achieve this goal, we adopt a queueing theoretic approach and propose a distributed fair scheduling algorithm for providing weighted fairness in single-hop networks. Our proposed algorithm guarantees an upper-bound on the normalized share disparity among any pair of agents. This can particularly improve the short-term fairness, which is important in real-time applications. Moreover, our scheduling algorithm adjusts itself dynamically to achieve a high throughput at the same time. The simulation results validate our claims and comparisons with the existing methods show our algorithm's superiority in providing short-term fairness, while achieving a high throughput.