Forward Networks, Inc.

Yasser Ganjisaffar

Sivasankar Radhakrishnan

Nikhil Handigol

Peyman Kazemian

Jared Jacobs

Andreas Voellmy

David Erickson

Brandon Heller

William S Whyte

Pete Crocker

Jasdeep Hundal

Qi Li

Behram Mistree

Yuefeng Liu

Natale Ruello

Chiara Regale

Braeden Partridge

Rachit Biyani

Matthias Schroeder

Braeden Patrirdge

Behran Mistree

William S White

Embodiments of the present invention provide improved methods, techniques, and systems to compute relevant and useful information that may be presented to users in an understandable, intuitive, and actionable platform. The interactive platform includes a format and visualization that is capable of presenting data for a wide range of protocols and topologies at various functional layers of the network. The interactive platform provides selectable categories of filters which update the network data and views displayed to the users to aid in the analysis and investigation of potential root causes of problems, rather than merely presenting examples of symptoms. The interactive platform includes a method and visualization that is capable of presenting differences in network behavior at various functional layers of the network.

Systems and methods for an interactive network analysis platform

Embodiments of the present invention can use a protocol-independent, vendor-independent, efficient and scalable abstraction model for representing the forwarding functionality of networks. Such a model can be used for systematic analysis and verification of networks. Packet header values may be represented as groups of one or more wildcarded bit strings, where unspecified header values are assumed to be fully wildcarded. This representation can describe many combinations of packets in a space-efficient way, enables more efficient tracing and transformation operations, and can even represent traffic from large internet routing tables efficiently. As a result of the scalability benefits of this more effective way to store and operate on packet collections, network modeling can scale to some of the largest, most complicated networks&#x2014;those where the benefits are the greatest.

Systems and methods for scalable network modeling

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Sunnyvale, CA, United States of America

Yasser Ganjisaffar