Loop-Free Backpressure Routing Using Link-Reversal Algorithms
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Anurag_MobiHoc_submission.pdf
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Author(s)
Rai, Anurag
Li, Chih-ping
Paschos, Georgios
Modiano, Eytan H.
Date Issued
June 2015
Journal
Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc '15)
Publisher
Association for Computing Machinery (ACM)
Citation
Anurag Rai, Chih-ping Li, Georgios Paschos, and Eytan Modiano. 2015. Loop-Free Backpressure Routing Using Link-Reversal Algorithms. In Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc '15). ACM, New York, NY, USA, 87-96.
Version
Original manuscript
Abstract
The backpressure routing policy is known to be a throughput optimal policy that supports any feasible traffic demand in data networks, but may have poor delay performance when packets traverse loops in the network. In this paper, we study loop-free backpressure routing policies that forward packets along directed acyclic graphs (DAGs) to avoid the looping problem. These policies use link reversal algorithms to improve the DAGs in order to support any achievable traffic demand.
For a network with a single commodity, we show that a DAG that supports a given traffic demand can be found after a finite number of iterations of the link-reversal process. We use this to develop a joint link-reversal and backpressure routing policy, called the loop free backpressure (LFBP) algorithm. This algorithm forwards packets on the DAG, while the DAG is dynamically updated based on the growth of the queue backlogs. We show by simulations that such a DAG-based policy improves the delay over the classical backpressure routing policy. We also propose a multicommodity version of the LFBP algorithm, and via simulation we show that its delay performance is better than that of backpressure.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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DOI of Published Version
https://doi.org/10.1145/2746285.2746304
