Computing the Optimal Longest Queue Length in Torus Networks

TL;DR

This paper addresses the problem of minimizing the longest queue length in directed torus networks by developing centralized and distributed algorithms that optimize switch scheduling, with proven bounds and efficient strategies.

Contribution

It introduces a conflict graph-based approach and algorithms for optimal queue length management in torus networks, including both centralized and local distributed methods.

Findings

Centralized algorithm achieves optimal queue length in $O(\sigma n)$ rounds.

Distributed algorithm attains optimal queue length in $O(\sigma C_{max}^2)$ rounds.

Conflict graph analysis shows no reduction in agents within conflict cycles when each link has at least 2 agents.

Abstract

A collection of $k$ mobile agents is arbitrarily deployed in the edges of a directed torus network where agents perpetually move to the successor edge. Each node has a switch that allows one agent of the two incoming edges to pass to its successor edge in every round. The goal is to obtain a switch scheduling to reach and maintain a configuration where the longest queue length is minimum. We consider a synchronous system. We use the concept of conflict graphs to model the local conflicts that occur with incident links. We show that there does not exist an algorithm that can reduce the number of agents in any conflict cycle of the conflict graph providing that all the links have at least 2 agents at every round. Hence, the lower bound is at least the average queue length of the conflict cycle with the maximum average queue length. Next, we present a centralized algorithm that computes a strategy in $O(n\log n)$ time for each round that attains the optimal queue length in $O(\sigma n)$ rounds where $n$ is the number of nodes in the network and $\sigma$ is the standard deviations of the queue lengths in the initial setting. Our technique is based on network flooding on conflict graphs. Next, we consider a distributed system where nodes have access to the length of their queues and use communication to self-coordinate with nearby nodes. We present a local algorithm using only the information of the queue lengths at distance two. We show that the algorithm attains the optimal queue length in $O(\sigma C_{max}^2)$ rounds where $C_{max}$ is the length of the longest conflict cycle with the maximum average queue length.