Mahboobeh Zangiabady | Searcharxiv

arXiv2024

Tracer: A Tool for Race Detection in Software Defined Network Models

Software Defined Networking (SDN) has become a new paradigm in computer networking, introducing a decoupled architecture that separates the network into the data plane and the control plane. The control plane acts as the centralized brain, managing configuration updates and network management tasks, while the data plane handles traffic based on the configurations provided by the control plane. Given its asynchronous distributed nature, SDN can experience data races due to message passing between the control and data planes. This paper presents Tracer, a tool designed to automatically detect and explain the occurrence of data races in DyNetKAT SDN models. DyNetKAT is a formal framework for modeling and analyzing SDN behaviors, with robust operational semantics and a complete axiomatization implemented in Maude. Built on NetKAT, a language leveraging Kleene Algebra with Tests to express data plane forwarding behavior, DyNetKAT extends these capabilities by adding primitives for communication between the control and data planes. Tracer exploits the DyNetKAT axiomatization and enables race detection in SDNs based on Lamport vector clocks. Tracer is a publicly available tool.

cs.FL↗

arXiv2023

Dash Sylvereye: A WebGL-powered Library for Dashboard-driven Visualization of Large Street Networks

State-of-the-art open network visualization tools like Gephi, KeyLines, and Cytoscape are not suitable for studying street networks with thousands of roads since they do not support simultaneously polylines for edges, navigable maps, GPU-accelerated rendering, interactivity, and the means for visualizing multivariate data. To fill this gap, the present paper presents Dash Sylvereye: a new Python library to produce interactive visualizations of primal street networks on top of tiled web maps. Thanks to its integration with the Dash framework, Dash Sylvereye can be used to develop web dashboards around temporal and multivariate street data by coordinating the various elements of a Dash Sylvereye visualization with other plotting and UI components provided by the Dash framework. Additionally, Dash Sylvereye provides convenient functions to easily import OpenStreetMap street topologies obtained with the OSMnx library. Moreover, Dash Sylvereye uses WebGL for GPU-accelerated rendering when redrawing the road network. We conduct experiments to assess the performance of Dash Sylvereye on a commodity computer when exploiting software acceleration in terms of frames per second, CPU time, and frame duration. We show that Dash Sylvereye can offer fast panning speeds, close to 60 FPS, and CPU times below 20 ms, for street networks with thousands of edges, and above 24 FPS, and CPU times below 40 ms, for networks with dozens of thousands of edges. Additionally, we conduct a performance comparison against two state-of-the-art street visualization tools. We found Dash Sylvereye to be competitive when compared to the state-of-the-art visualization libraries Kepler.gl and city-roads. Finally, we describe a web dashboard application that exploits Dash Sylvereye for the analysis of a SUMO vehicle traffic simulation.

cs.HC↗