Bayonet: Probabilistic inference for networks

Timon Gehr, Sasa Misailovic, Petar Tsankov, Laurent Vanbever, Pascal Wiesmann, Martin Vechev

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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).

Original languageEnglish (US)
Title of host publicationPLDI 2018 - Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation
EditorsJeffrey S. Foster, Dan Grossman, Jeffrey S. Foster
PublisherAssociation for Computing Machinery
Pages586-602
Number of pages17
ISBN (Electronic)9781450356985
DOIs
StatePublished - Jun 11 2018
Event39th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2018 - Philadelphia, United States
Duration: Jun 18 2018Jun 22 2018

Publication series

NameProceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)

Other

Other39th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2018
CountryUnited States
CityPhiladelphia
Period6/18/186/22/18

Keywords

  • Computer networks
  • Probabilistic programming

ASJC Scopus subject areas

  • Software

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  • Cite this

    Gehr, T., Misailovic, S., Tsankov, P., Vanbever, L., Wiesmann, P., & Vechev, M. (2018). Bayonet: Probabilistic inference for networks. In J. S. Foster, D. Grossman, & J. S. Foster (Eds.), PLDI 2018 - Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation (pp. 586-602). (Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)). Association for Computing Machinery. https://doi.org/10.1145/3192366.3192400