research-article

Bayonet: probabilistic inference for networks

Authors:

Sasa Misailovic,

Laurent Vanbever,

Pascal Wiesmann,

Martin VechevAuthors Info & Claims

PLDI 2018: Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 586 - 602

https://doi.org/10.1145/3192366.3192400

Published: 11 June 2018 Publication History

Abstract

Network operators often need to ensure that important probabilistic properties are met, such as that the probability of network congestion is below a certain threshold. Ensuring such properties is challenging and requires both a suitable language for probabilistic networks and an automated procedure for answering probabilistic inference queries.

We present Bayonet, a novel approach that consists of: (i) a probabilistic network programming language and (ii) a system that performs probabilistic inference on Bayonet programs. The key insight behind Bayonet is to phrase the problem of probabilistic network reasoning as inference in existing probabilistic languages. As a result, Bayonet directly leverages existing probabilistic inference systems and offers a flexible and expressive interface to operators.

We present a detailed evaluation of Bayonet on common network scenarios, such as network congestion, reliability of packet delivery, and others. Our results indicate that Bayonet can express such practical scenarios and answer queries for realistic topology sizes (with up to 30 nodes).

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Index Terms

Bayonet: probabilistic inference for networks
1. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic reasoning algorithms
2. Networks
  1. Network performance evaluation
    1. Network simulations

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cover image ACM Conferences

PLDI 2018: Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2018

825 pages

ISBN:9781450356985

DOI:10.1145/3192366

General Chair:
Jeffrey S. Foster
University of Maryland at College Park, USA
,
Program Chair:
Dan Grossman
University of Washington, USA

ACM SIGPLAN Notices Volume 53, Issue 4
PLDI '18
April 2018
834 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/3296979
Editor:
Matthew Fluet
Rodchester Institude of Technology
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https://dl.acm.org/doi/10.1145/3544216.3544220
Giannarakis NSilva AWalker D(2021)ProbNV: probabilistic verification of network control planesProceedings of the ACM on Programming Languages10.1145/34735955:ICFP(1-30)Online publication date: 19-Aug-2021
https://dl.acm.org/doi/10.1145/3473595
Kang QXing JQiu YChen ASherwood TBerger EKozyrakis C(2021)Probabilistic profiling of stateful data planes for adversarial testingProceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3445814.3446764(286-301)Online publication date: 19-Apr-2021
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