Computing Delay-Constrained Least-Cost Paths for Segment Routing is Easier Than You Think
Jean-Romain Luttringer , Thomas Alfroy , Pascal Mérindol , Quentin Bramas , François Clad and Cristel Pelsser
Abstract
With the growth of demands for quasi-instantaneous communication services such as real-time video streaming, cloud gaming, and industry 4.0 applications, multi-constraint Traffic Engineering (TE) becomes increasingly important. While legacy TE management planes have proven laborious to deploy, Segment Routing (SR) drastically eases the deployment of TE paths and thus became the most appropriate technology for many operators. The flexibility of SR sparked demands in ways to compute more elaborate paths. In particular, there exists a clear need in computing and deploying Delay-Constrained Least-Cost paths (DCLC) for real-time applications requiring both low delay and high bandwidth routes. However, most current DCLC solutions are heuristics not specifically tailored for SR. In this work, we leverage both inherent limitations in the accuracy of delay measurements and an operational constraint added by SR. We include these characteristics in the design of BEST2COP, an exact but efficient ECMP-aware algorithm that natively solves DCLC in SR domains. Through an extensive performance evaluation, we first show that BEST2COP scales well even in large random networks. In real networks having up to thousands of destinations, our algorithm returns all DCLC solutions encoded as SR paths in way less than a second.
Publication Details
- Publication Type
- Journal Article
- Publication Date
- November 2020
- Published In
- IEEE International Symposium on Network Computing and Applications
- Volume & Issue
- Vol. abs/2011.05191
- Location
- On-Line, France
- Digital Object Identifier (DOI)
- 10.1109/nca51143.2020.9306706
BibTeX Citation
@article{Luttringer2020,
title = {Computing Delay-Constrained Least-Cost Paths for Segment Routing is Easier Than You Think},
author = {Jean-Romain Luttringer and Thomas Alfroy and Pascal Mérindol and Quentin Bramas and François Clad and Cristel Pelsser},
year = 2020,
month = nov,
journal = {IEEE International Symposium on Network Computing and Applications},
address = {On-Line, France},
volume = {abs/2011.05191},
doi = {10.1109/nca51143.2020.9306706},
abstract = {With the growth of demands for quasi-instantaneous communication services such as real-time video streaming, cloud gaming, and industry 4.0 applications, multi-constraint Traffic Engineering (TE) becomes increasingly important. While legacy TE management planes have proven laborious to deploy, Segment Routing (SR) drastically eases the deployment of TE paths and thus became the most appropriate technology for many operators. The flexibility of SR sparked demands in ways to compute more elaborate paths. In particular, there exists a clear need in computing and deploying Delay-Constrained Least-Cost paths (DCLC) for real-time applications requiring both low delay and high bandwidth routes. However, most current DCLC solutions are heuristics not specifically tailored for SR. In this work, we leverage both inherent limitations in the accuracy of delay measurements and an operational constraint added by SR. We include these characteristics in the design of BEST2COP, an exact but efficient ECMP-aware algorithm that natively solves DCLC in SR domains. Through an extensive performance evaluation, we first show that BEST2COP scales well even in large random networks. In real networks having up to thousands of destinations, our algorithm returns all DCLC solutions encoded as SR paths in way less than a second.},
archiveprefix = {arXiv},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/journals/corr/abs-2011-05191.bib},
eprint = {2011.05191},
eprinttype = {arxiv},
file = {:Luttringer2020 - Computing Delay Constrained Least Cost Paths for Segment Routing Is Easier Than You Think.pdf:PDF},
groups = {International Conferences},
keywords = {Delays, Measurement, Routing, Bandwidth, Real-time systems, Complexity theory, Propagation delay},
primaryclass = {cs.NI}
}
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