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A Fast Quantitative Analyzer for NetKAT

Thomas Lu, Qiancheng Fu, Kevin Batz, Oliver Bøving 2026-07-18
Network Design Data Search ×

The problem is that network engineers need to reason about quantitative trade-offs like bandwidth, latency, and resilience, which existing tools do not support. The method introduces a fast analyzer using weighted NetKAT (wNetKAT) and a symbolic data structure called weighted symbolic packet programs (wSPPs) to compactly represent and compute quantitative network policies. Experimental evidence shows the Rust implementation is competitive with KATch on Boolean reachability and orders of magnitude faster than McNetKAT and Storm on probabilistic analyses, with a case study on Fat-tree and Jellyfish topologies demonstrating multi-objective design-time analysis. This matters because it provides a practical, parametric framework for fast quantitative reasoning about network properties, enabling engineers to explore design trade-offs at scale.

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Hybrid multi-objective evolutionary algorithms for service placement in the computing continuum: a comparative study with genetic traceability

Sergi Vivo, Carlos Guerrero, Isaac Lera 2026-07-18
Design Search × Distributed Architecture

This paper addresses the multi-objective service placement problem in computing continuum environments by proposing a collaborative hybrid island-model MOEA. The method systematically applies heterogeneous hybridization across two experimental campaigns, co-evolving state-of-the-art MOEAs like NSGA-II, NSGA-III, and SMS-EMOA with periodic solution exchange. Across 30 independent runs, the hybrid method outperforms most standalone baselines, with statistical tests confirming significant improvements and traceability analysis revealing non-uniform contributions among islands. This matters because it demonstrates that hybrid cooperation yields scalable, interpretable performance gains for distributed edge-fog-cloud architectures, enabling more efficient service placement in the computing continuum.

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