@INPROCEEDINGS{schneider2025verifying,
	isbn = {978-1-939133-46-5},
	copyright = {In Copyright - Non-Commercial Use Permitted},
	year = {2025-04},
	booktitle = {Proceedings of the 22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI ’25)},
	type = {Conference Paper},
	institution = {EC},
	author = {Schneider, Tibor and Vissicchio, Stefano and Vanbever, Laurent},
	abstract = {To meet ever more stringent requirements, network operators often need to reason about worst-case link loads. Doing so involves analyzing traffic forwarding after failures and BGP route changes. State-of-the-art systems identify failure scenarios causing congestion, but they ignore route changes.We present Viper, the first verification system that efficiently finds maximum link loads under failures and route changes. The key building block of Viper is its ability to massively reduce the gigantic space of possible route changes thanks to (i) a router-based abstraction for route changes, (ii) a theoretical characterization of scenarios leading to worst-case link loads, and (iii) an approximation of input traffic matrices. We fully implement and extensively evaluate Viper. Viper takes only a few minutes to accurately compute all worst-case link loads in large ISP networks. It thus provides operators with critical support to robustify network configurations, improve network management and take business decisions.},
	language = {en},
	address = {Berkeley, CA},
	publisher = {USENIX Association},
	DOI = {10.3929/ethz-b-000711664},
	title = {Verifying maximum link loads in a changing world},
	PAGES = {1269 - 1287},
	Note = {22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI 2025); Conference Location: Philadelphia, PA, USA; Conference Date: April 28-30, 2025}
}

@INPROCEEDINGS{schneider2023taming,
	isbn = {979-8-4007-0236-5},
	copyright = {Creative Commons Attribution 4.0 International},
	doi = {10.3929/ethz-b-000612650},
	year = {2023-09},
	booktitle = {ACM SIGCOMM '23: Proceedings of the ACM SIGCOMM 2023 Conference},
	type = {Conference Paper},
	institution = {EC and EC},
	author = {Schneider, Tibor and Schmid, Roland and Vissicchio, Stefano and Vanbever, Laurent},
	abstract = {BGP reconfigurations are a daily occurrence for most network operators, especially in large networks. Yet, performing safe and robust BGP reconfiguration changes is still an open problem. Few BGP reconfiguration techniques exist, and they are either (i) unsafe, because they ignore transient states, which can easily lead to invariant violations; or (ii) impractical, as they duplicate the entire routing and forwarding states, and require special hardware. In this paper, we introduce Chameleon, the first BGP reconfiguration framework capable of maintaining correctness throughout a reconfiguration campaign while relying on standard BGP functionalities and minimizing state duplication. Akin to concurrency coordination in distributed systems, Chameleon models the reconfiguration process with happens-before relations. This modeling allows us to capture the safety properties of transient BGP states. We then use this knowledge to precisely control the BGP route propagation and convergence, so that input invariants are provably preserved at any time during the reconfiguration. We fully implement Chameleon and evaluate it in both testbeds and simulations, on real-world topologies and large-scale reconfiguration scenarios. In most experiments, our system computes reconfiguration plans within a minute, and performs them from start to finish in a few minutes, with minimal overhead.},
	language = {en},
	address = {New York, NY},
	publisher = {Association for Computing Machinery},
	title = {Taming the transient while reconfiguring BGP},
	PAGES = {77 - 93},
	Note = {37th ACM SIGCOMM Conference (SIGCOMM 2023); Conference Location: New York, NY, USA; Conference Date: September 10-14, 2023}
}

@inproceedings{schneider2022complexity,
	isbn = {978-1-6654-8234-9},
	doi = {10.1109/ICNP55882.2022.9940325},
	year = {2022},
	booktitle = {2022 IEEE 30th International Conference on Network Protocols (ICNP)},
	type = {Conference Paper},
	institution = {EC},
	author = {Schneider, Tibor and Schmid, Roland and Vanbever, Laurent},
	size = {11 p.},
	abstract = {Configuration Synthesis promises to increase automation in network hardware configuration but is generally assumed to constitute a computationally hard problem. We conduct a formal analysis of the computational complexity of network-wide Configuration Synthesis to establish this claim formally. To that end, we consider Configuration Synthesis as a decision problem, whether or not the selected routing protocol(s) can implement a given set of forwarding properties. We find the complexity of Configuration Synthesis heavily depends on the combination of the forwarding properties that need to be implemented in the network, as well as the employed routing protocol(s). Our analysis encompasses different forwarding properties that can be encoded as path constraints, and any combination of distributed destination-based hop-by-hop routing protocols. Many of these combinations yield NP-hard Configuration Synthesis problems; in particular, we show that the satisfiability of a set of arbitrary waypoints for any hop-by-hop routing protocol is NP-complete. Other combinations, however, show potential for efficient, scalable Configuration Synthesis.},
	keywords = {Automation; Computational modeling; Routing; Routing protocols; Hardware; Space exploration; Internet},
	language = {en},
	address = {Piscataway, NJ},
	publisher = {IEEE},
	title = {On the Complexity of Network-Wide Configuration Synthesis},
	PAGES = {9940325},
	Note = {30th IEEE International Conference on Network Protocols (ICNP 2022); Conference Location: Lexington, KY, USA; Conference Date: October 30 - November 2, 2022}
}

@inproceedings{schneider2021snowcap,
	isbn = {978-1-4503-8383-7},
	copyright = {In Copyright - Non-Commercial Use Permitted},
	doi = {10.3929/ethz-b-000491508},
	year = {2021-08},
	booktitle = {Proceedings of the 2021 ACM SIGCOMM Conference},
	type = {Conference Paper},
	institution = {EC},
	author = {Schneider, Tibor and Birkner, Rüdiger and Vanbever, Laurent},
	abstract = {Large-scale reconfiguration campaigns tend to be nerve-racking for network operators as they can lead to significant network downtimes, decreased performance, and policy violations. Unfortunately, existing reconfiguration frameworks often fall short in practice as they either only support a small set of reconfiguration scenarios or simply do not scale. We address these problems with Snowcap, the first network reconfiguration framework which can synthesize configuration updates that comply with arbitrary hard and soft specifications, and involve arbitrary routing protocols. Our key contribution is an efficient search procedure which leverages counter-examples to efficiently navigate the space of configuration updates. Given a reconfiguration ordering which violates the desired specifications, our algorithm automatically identifies the problematic commands so that it can avoid this particular order in the next iteration. We fully implemented Snowcap and extensively evaluated its scalability and effectiveness on real-world topologies and typical, large-scale reconfiguration scenarios. Even for large topologies, Snowcap finds a valid reconfiguration ordering with minimal side-effects (i.e., traffic shifts) within a few seconds at most.},
	keywords = {Network analysis; Configuration; Migration},
	language = {en},
	address = {New York, NY},
	publisher = {Association for Computing Machinery},
	title = {Snowcap: Synthesizing Network-Wide Configuration Updates},
	PAGES = {33 - 49},
	Note = {ACM SIGCOMM 2021 Conference; Conference Location: Online; Conference Date: August 23-27, 2021}
}

@inproceedings{schneider2020qeegnet,
	author={Schneider, Tibor and Wang, Xiaying and Hersche, Michael and Cavigelli, Lukas and Benini, Luca},
	booktitle={2020 IEEE International Conference on Smart Computing (SMARTCOMP)},
	title={Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain-Machine Interfaces},
	year={2020},
	pages={284-289},
	keywords={Deep learning;Computational modeling;Energy efficiency;Electroencephalography;Brain-computer interfaces;Classification algorithms;Optimization;brain-machine interface;edge computing;parallel computing;machine learning;deep learning;motor imagery},
	doi={10.1109/SMARTCOMP50058.2020.00065}
}