Throughput-optimal multi-hop broadcast algorithms
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Modiano_Throughput-optimal multi.pdf
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Author(s)
Paschos, Georgios
Sinha, Abhishek
Modiano, Eytan H
Date Issued
July 2016
Journal
Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing - MobiHoc '16
Publisher
Association for Computing Machinery (ACM)
Citation
Sinha, Abhishek, Georgios Paschos, and Eytan Modiano. “Throughput-Optimal Multi-Hop Broadcast Algorithms.” 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 5-8 July, 2016, Paderborn, Germany, ACM Press, 2016. 51–60.
Version
Original manuscript
Abstract
In this paper we design throughput-optimal dynamic broadcast algorithms for multi-hop networks with arbitrary topologies. Most of the previous broadcast algorithms route packets along spanning trees, rooted at the source node. For large time-varying 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 make packet-by-packet scheduling and routing decisions and hence, obviate the need for maintaining any global topological structures, such as spanning trees. Our algorithms may be conveniently understood as a non-trivial generalization of the familiar back-pressure algorithm, which makes unicast packet routing and scheduling decisions, based on local queue-length information and does not require to maintain end-to-end paths. However, in the broadcast setting, due to packet duplications, it is hard to define appropriate queuing structures. We design and prove the optimality of a virtual-queue based algorithm, where virtual-queues are defined for subsets of nodes. We then propose a multi-class broadcast policy which combines the above scheduling algorithm with in-class-in-order packet forwarding, resulting in significant reduction in complexity. Finally, we evaluate performance of the proposed algorithms via extensive numerical simulations.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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DOI of Published Version
https://doi.org/10.1145/2942358.2942390
