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).
Fri 22 Jun
|11:00 - 11:25|
Marco Cusumano-TownerMIT-CSAIL, Benjamin BichselETH Zurich, Switzerland, Timon Gehr, Martin VechevETH Zürich, Vikash MansinghkaMITMedia Attached
|11:25 - 11:50|
Timon Gehr, Sasa MisailovicUniversity of Illinois at Urbana-Champaign, USA, Petar TsankovETH Zurich, Laurent VanbeverETH Zürich, Pascal WiesmannETH Zurich, Switzerland, Martin VechevETH ZürichMedia Attached
|11:50 - 12:15|
Vikash MansinghkaMIT, Ulrich SchaechtleMassachusetts Institute of Technology, USA, Shivam Handa, Alexey Radul, Yutian ChenGoogle Deepmind, n.n., Martin RinardMassachusetts Institute of TechnologyMedia Attached