research-article

Lancet: Better Network Resilience by Designing for Pruned Failure Sets

Authors:

Ashish Chandra,

Mohit TawarmalaniAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 3, Issue 3

Article No.: 49, Pages 1 - 26

https://doi.org/10.1145/3366697

Published: 17 December 2019 Publication History

Abstract

Recently, researchers have started exploring the design of route protection schemes that ensure networks can sustain traffic demand without congestion under failures. Existing approaches focus on ensuring worst-case performance over simultaneous f-failure scenarios is acceptable. Unfortunately, even a single bad scenario may render the schemes unable to protect against any f-failure scenario. In this paper, we present Lancet, a system designed to handle most failures when not all can be tackled. Lancet comprises three components: (i) an algorithm to analyze which failure scenarios the network can intrinsically handle which provides a benchmark for any protection routing scheme, and guides the design of new schemes; (ii) an approach to efficiently design a protection schemes for more general failure sets than all f-failure scenarios; and (iii) techniques to determine which of combinatorially many scenarios to design for. Our evaluations with real topologies and validations on an emulation testbed show that Lancet outperforms a worst-case approach by protecting against many more scenarios, and can even match the scenarios that can be handled by optimal network response.

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Index Terms

Lancet: Better Network Resilience by Designing for Pruned Failure Sets
1. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Traffic engineering algorithms
  2. Network performance evaluation

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 3, Issue 3

SIGMETRICS

December 2019

525 pages

EISSN:2476-1249

DOI:10.1145/3376928

Editors:
Augustin Chaintreau
Columbia University
,
Leana Golubchik
University of Southern California
,
Zhi-Li Zhang
University of Minnesota

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Copyright © 2019 ACM.

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Publication History

Published: 17 December 2019

Published in POMACS Volume 3, Issue 3

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https://dl.acm.org/doi/10.1145/3651890.3672237
Liu CAggarwal VLan TGeng NYang YXu MLi Q(2024)FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network DesignIEEE/ACM Transactions on Networking10.1109/TNET.2023.331167832:2(1003-1018)Online publication date: Apr-2024
https://doi.org/10.1109/TNET.2023.3311678
Wu BQian KLi BMa YZhang QJiang ZZhao JCai DZhai ELiu XJin XSchulzrinne HKohler EMaltz DMisra V(2023)XRON: A Hybrid Elastic Cloud Overlay Network for Video Conferencing at Planetary ScaleProceedings of the ACM SIGCOMM 2023 Conference10.1145/3603269.3604845(696-709)Online publication date: 10-Sep-2023
https://dl.acm.org/doi/10.1145/3603269.3604845
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Liu CAggarwal VLan TGeng NYang YXu M(2023)Machine Learning for Robust Network Design: A New PerspectiveIEEE Communications Magazine10.1109/MCOM.002.220067061:10(86-92)Online publication date: Oct-2023
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