Google Inc.

Forward Networks, Inc.

Sivasankar Radhakrishnan

Nikhil Handigol

Peyman Kazemian

Yuchung Cheng

Yasser Ganjisaffar

David Erickson

Brandon Heller

Glen Anderson

Jared Jacobs

Andreas Voellmy

Chiara Regale

Pete Crocker

Jasdeep Hundal

Qi Li

Behram Mistree

Yuefeng Liu

Natale Ruello

William S Whyte

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

Embodiments relate generally to network hardware, network software and methods for network management and testing. In some embodiments, state information (e.g., configuration data, forwarding states, IP tables, rules, network topology information, etc.) can be received from devices in a network. The state information can be parsed and used to generate a network model, which describes how data is processed by the network. Using the model, possible flow paths of data through the network can be identified and used to analyze the network and identify network behavior, such as types of traffic, frequency of rule matches, what kind of transformation occurs as traffic flows through the network, and where the traffic gets dropped, etc. Policies can be verified against the network model to ensure compliance, and in the event of non-compliance, a report or interface can indicate the cause and/or allow a user to explore specific details about the cause.

Systems and methods for network management

Techniques and mechanisms for evaluating a transmission outcome indicium to determine a state of data traffic congestion in a network. In an embodiment, congestion information for a packet of a transmission group is evaluated at a computer device to determine a congestion state, where a size of the transmission group is determined based on a congestion window parameter. In another embodiment, the evaluation to determine the congestion state is performed prior to the computer device detecting a transmission outcome indicium for some other packet of the transmission group.

Congestion window modification

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for updating shared keys. In one aspect, a method includes generating, at a first server and using a first key associated with a current period of time on the first server, a first piece of information based on a first request received from a first client device; receiving, at a second server, a second request from the first client device, the second request including the generated first piece of information; and validating, at the second server and using the first key, the first piece of information, wherein the validating is performed during the current period of time on the second server and wherein to generate information the second server uses a second key different from the first key.

Updating shared keys

Growing community of inventors

Sunnyvale, CA, United States of America

Sivasankar Radhakrishnan