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OptFlow: A Flow-based Abstraction for Programmable Topologies

Authors:

Klaus-Tycho Foerster,

Manya GhobadiAuthors Info & Claims

SOSR '20: Proceedings of the Symposium on SDN Research

Pages 96 - 102

https://doi.org/10.1145/3373360.3380840

Published: 04 March 2020 Publication History

Abstract

The rapid adoption of Reconfigurable Optical Add-Drop Multiplexers (ROADMs) is setting the stage for the dynamic reconfiguration of the network topology in optical backbones. The conventional approach to enable programmability in the physical layer requires solving a cross-layer optimization formulation that captures the interplay between the IP and optical layers. However, as the network scales, the complexity and run time of cross-layer optimization formulations grow prohibitively, resulting in heuristic-based solutions that sacrifice optimality for scalability. We propose a flow-based graph abstraction, called OptFlow, that is able to find the optimal allocation faster than a cross-layer optimization formulation. The key idea in OptFlow is that topology programmability is abstracted by "network flows," enabling service providers to use multi-commodity flow formulations, such as conventional Traffic Engineering, to solve a cross-layer optimization. OptFlow augments the physical graph and uses it as input to the unmodified flow-based Traffic Engineering algorithm, capturing a variety of IP-layer optimization goals such as max throughput, min hop count, and max-min fairness. Due to its flow-based nature, OptFlow inherently provides an abstraction for consistent network updates. To benchmark our key assumptions in OptFlow, we build a small testbed prototype consisting of four ROADMs. To evaluate the optimality and run time of large networks, we simulate five WAN topologies with up to 100 nodes and 390 links. Our results show that OptFlow matches the throughput performance of an optimal cross-layer formulation but has faster computation times. The run time speed-up of OptFlow increases as the network scales, with up to 8x faster execution times in our simulations.

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Cited By

Ding WXu H(2024)Dynamic Learning-based Link Restoration in Traffic Engineering with ArchieIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621357(2428-2437)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621357
Dai WDinitz MFoerster KLuo LSchmid S(2024)Approximation Algorithms for Minimizing Congestion in Demand-Aware NetworksIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621340(1461-1470)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621340
Prakash DManaguli M(2024)An Optimization Approach to DWDM Network Reconfiguration through Reinforcement LearningSN Computer Science10.1007/s42979-024-03438-45:8Online publication date: 20-Nov-2024
https://doi.org/10.1007/s42979-024-03438-4
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Index Terms

OptFlow: A Flow-based Abstraction for Programmable Topologies
1. Networks
  1. Network properties
    1. Network range
      1. Wide area networks
  2. Network services
    1. Programmable networks
2. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis
      1. Network flows

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cover image ACM Conferences

SOSR '20: Proceedings of the Symposium on SDN Research

March 2020

151 pages

ISBN:9781450371018

DOI:10.1145/3373360

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 04 March 2020

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SOSR '20: Symposium on SDN Research

March 3, 2020

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Overall Acceptance Rate 7 of 43 submissions, 16%

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Cited By

Ding WXu H(2024)Dynamic Learning-based Link Restoration in Traffic Engineering with ArchieIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621357(2428-2437)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621357
Dai WDinitz MFoerster KLuo LSchmid S(2024)Approximation Algorithms for Minimizing Congestion in Demand-Aware NetworksIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621340(1461-1470)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621340
Prakash DManaguli M(2024)An Optimization Approach to DWDM Network Reconfiguration through Reinforcement LearningSN Computer Science10.1007/s42979-024-03438-45:8Online publication date: 20-Nov-2024
https://doi.org/10.1007/s42979-024-03438-4
Liu LSong LChen XYu HSun G(2022)Accelerating model synchronization for distributed machine learning in an optical wide area networkJournal of Optical Communications and Networking10.1364/JOCN.46228614:10(852)Online publication date: 27-Sep-2022
https://doi.org/10.1364/JOCN.462286
Fenz TFoerster KSchmid S(2021)On Efficient Oblivious Wavelength Assignments for Programmable Wide-Area TopologiesProceedings of the Symposium on Architectures for Networking and Communications Systems10.1145/3493425.3502753(38-51)Online publication date: 13-Dec-2021
https://dl.acm.org/doi/10.1145/3493425.3502753
Zhong ZGhobadi MKhaddaj ALeach JXia YZhang YKuipers FCaesar M(2021)ARROWProceedings of the 2021 ACM SIGCOMM 2021 Conference10.1145/3452296.3472921(560-579)Online publication date: 9-Aug-2021
https://dl.acm.org/doi/10.1145/3452296.3472921
Liu LYu HSun G(2021)Reconfigurable Aggregation Tree for Distributed Machine Learning in Optical WAN2021 3rd International Conference on Applied Machine Learning (ICAML)10.1109/ICAML54311.2021.00051(206-210)Online publication date: Jul-2021
https://doi.org/10.1109/ICAML54311.2021.00051
Hall MFoerster KSchmid SDurairajan R(2021)A Survey of Reconfigurable Optical NetworksOptical Switching and Networking10.1016/j.osn.2021.100621(100621)Online publication date: Mar-2021
https://doi.org/10.1016/j.osn.2021.100621
Luo LFoerster KSchmid SYu H(2020)Deadline-Aware Multicast Transfers in Software-Defined Optical Wide-Area NetworksIEEE Journal on Selected Areas in Communications10.1109/JSAC.2020.2986904(1-1)Online publication date: 2020
https://doi.org/10.1109/JSAC.2020.2986904

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