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International Conference on Verification, Model Checking, and Abstract Interpretation

VMCAI 2021: Verification, Model Checking, and Abstract Interpretation pp 486–508Cite as

Netter: Probabilistic, Stateful Network Models

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12597))

Abstract

We study the problem of using probabilistic network models to formally analyze their quantitative properties, such as the effect of different load-balancing strategies on the long-term traffic on a server farm. Compared to prior work, we explore a different design space in terms of tradeoffs between model expressiveness and analysis scalability, which we realize in a language we call Netter. Netter code is compiled to probabilistic automata, undergoing optimization passes to reduce the state space of the generated models, thus helping verification scale. We evaluate Netter on several case studies, including a probabilistic load balancer, a routing scheme reminiscent of MPLS, and a network defense mechanism against link-flooding attacks. Our results show that Netter can analyze quantitative properties of interesting routing schemes that prior work hadn’t addressed, for networks of small size (4–9 nodes and a few different types of flows). Moreover, when specialized to simpler, stateless networks, Netter can parallel the performance of previous state-of-the-art tools, scaling up to millions of nodes.

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Notes

  1. 1.

    Due to dependency issues, we only managed to run part of the experiment of Smolka et al. [27], so our comparison is mostly based on the numbers reported by the authors. While this prevents us from making a precise comparison, their setup was similar to ours, and we do not expect the performance of their code to change substantially.

  2. 2.

    In principle, since variables are bounded, it would be possible to do away with expressions by evaluating them at every possible state. However, this would result in much larger compiled models, making the analysis more costly.

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Acknowledgments

The authors would like to thank Steffen Smolka, Justin Hsu and Timon Gehr for useful discussions and clarifications. This work was partially funded by ONR award N000141812618, NSF award 1513961 and NSF award 1564009.

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Han Zhang, Chi Zhang, Arthur Azevedo de Amorim, Yuvraj Agarwal, Matt Fredrikson & Limin Jia

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  1. Han Zhang
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Correspondence to Han Zhang .

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Editors and Affiliations

  1. University of Copenhagen, Copenhagen, Denmark

    Fritz Henglein

  2. Tel Aviv University, Tel Aviv, Israel

    Sharon Shoham

  3. Technion, Haifa, Israel

    Yakir Vizel

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Zhang, H., Zhang, C., Azevedo de Amorim, A., Agarwal, Y., Fredrikson, M., Jia, L. (2021). Netter: Probabilistic, Stateful Network Models. In: Henglein, F., Shoham, S., Vizel, Y. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2021. Lecture Notes in Computer Science(), vol 12597. Springer, Cham. https://doi.org/10.1007/978-3-030-67067-2_22

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  • DOI: https://doi.org/10.1007/978-3-030-67067-2_22

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