Throughput-Optimal Multi-hop Broadcast Algorithms

TL;DR

This paper introduces throughput-optimal dynamic broadcast algorithms for multi-hop networks that avoid maintaining global topological structures, using virtual queues and class-based policies to improve efficiency and scalability.

Contribution

It presents a novel class of algorithms that generalize back-pressure routing for broadcast, eliminating the need for spanning trees and handling dynamic topologies effectively.

Findings

Algorithms achieve throughput optimality in multi-hop broadcast.

Significant reduction in complexity with class-based in-order delivery.

Extensive simulations demonstrate strong performance.

Abstract

In this paper we design throughput-optimal dynamic broad- cast algorithms for multi-hop networks with arbitrary topolo- gies. Most of the previous broadcast algorithms route pack- ets along spanning trees, rooted at the source node. For large dynamic networks, computing and maintaining a set of spanning trees is not efficient, as the network-topology may change frequently. In this paper we design a class of dynamic algorithms which makes packet-by-packet schedul- ing and routing decisions and thus obviates the need for maintaining any global topological structures, such as span- ning trees. Our algorithms may be conveniently understood as a non-trivial generalization of the familiar back-pressure algorithm which makes unicast packet routing and schedul- ing decisions, based on queue-length information, without maintaining end-to-end paths. However, in the broadcast problem, it is hard to define queuing structures due to ab- sence of a work-conservation principle which results from packet duplications. We design and prove the optimality of a virtual-queue based algorithm, where a virtual-queue is de- fined for subsets of vertices. We then propose a multi-class broadcast policy which combines the above scheduling algo- rithm with a class-based in-order packet delivery constraint, resulting in significant reduction in complexity. Finally, we evaluate performance of the proposed algorithms via exten- sive numerical simulations.