Problem
Research questions and friction points this paper is trying to address.
quantitative network analysis
network design trade-offs
multi-objective optimization
NetKAT
semiring-based reasoning
A Fast Quantitative Analyzer for NetKAT
🤖 AI Summary
Network design entails trade-offs among quantitative attributes such as bandwidth, latency, and resilience, yet efficient analytical tools for such multi-objective reasoning remain scarce. This work proposes weighted Symbolic Packet Programs (wSPP) and trajectory-carrying Pareto semirings, grounded in weighted NetKAT, to compactly represent network policy semantics via symbolic data structures and enable joint computation of multi-objective Pareto fronts along with their corresponding paths. The approach integrates symbolic execution, a customized Kleene star algorithm, and semiring-based parametric modeling; its core implementation is written in Rust, with formal verification conducted in Lean. Experimental results demonstrate that the method matches KATch in Boolean reachability analysis, outperforms McNetKAT and Storm by several orders of magnitude in probabilistic analysis, and successfully supports multi-objective comparisons on Fat-tree and Jellyfish topologies.
📝 Abstract
When designing a network, engineers must navigate trade-offs (e.g., one topology offers more aggregate bandwidth, another lower latency or better resilience) that demand reasoning about quantitative properties. We present a fast analyzer for quantitative network properties based on weighted NetKAT (wNetKAT), a domain-specific language that provides a semantic foundation for quantitative reasoning by modeling network behavior using weights drawn from a semiring. At the core of our development is the design of a symbolic data structure -- weighted symbolic packet programs (wSPPs) -- that compactly represent the semantics of weighted policies, for which a direct implementation would be intractable. We show how to compute all policy constructs symbolically; unsurprisingly, the crux is Kleene star, for which we design a tailored algorithm. We further develop trace-carrying Pareto semirings, which compute multi-objective frontiers together with the network paths that realize them. We formalize the development in Lean and provide an optimized Rust implementation. Being parametric on a semiring, our implementation covers both classical and quantitative analyses: we show that it is competitive with KATch, a heavily optimized Boolean-reachability verifier, and orders of magnitude faster than McNetKAT and Storm on probabilistic analyses. A case study comparing Fat-tree and Jellyfish data-center topologies shows the framework supports multi-objective design-time analysis.
Innovation
Methods, ideas, or system contributions that make the work stand out.
wNetKAT
symbolic data structure
Pareto semiring
multi-objective analysis
Kleene star algorithm
🔎 Similar Papers
No similar papers found.
