Bayonet: probabilistic inference for networks

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).

Scalable verification of probabilistic networks

“…To evaluate McNetKAT we conducted experiments on several benchmarks including a family of real-world data center topologies and a synthetic benchmark drawn from the literature [17]. We evaluated McNetKAT's scalability, characterized the effect of optimizations, and compared performance against other state-of-the-art tools.…”

“…Although programming languages for describing randomized networks exist [13,17], support for automated reasoning remains limited. Even basic properties require quantitative reasoning in the probabilistic setting, and seemingly simple programs can generate complex distributions.…”

“…To evaluate the scalability of McNetKAT, we conducted experiments on realistic topologies, routing schemes, and properties. Our results show that McNetKAT scales to networks with thousands of switches, and performs orders of magnitude better than a state-of-the-art tool based on general-purpose symbolic inference [17,18]. We also used McNetKAT to carry out a case study of the resilience of a fault-tolerant data center design proposed by Liu et al [34].…”

Inferring Catchment in Internet Routing

“…Hence, in case the route for this node is not already inferred (f (CP i ) = 0), there is a new inference for this node and S R is called. After making the inferences for the parents of i (lines 13-15), the algorithm proceeds to inference for the children nodes of i (lines [16][17][18][19][20][21][22][23][24][25][26]. For each child j without an inferred route (line 16), it collects the distinct values of the routing function of its parents P j (lines [18][19][20][21][22].…”

“…Our framework can be used complementary to these works. Finally, probabilistic network programming languages [18], which capture probabilistic network behavior and analyze it through standard probabilistic inference methods, could be combined with our work to design novel efficient inference tools for Internet routing applications.…”