Packeteer, Inc.

Juniper Networks, Inc.

Li Fang

Farzad Ghannadian

Guy G Riddle

Shauli Gal

Gopi Krishna

Suresh Kumar Vinapamula Venkata

Michael J Quinn

Sanjay Agrawal

Harsha Srinath

Michael Robert Morford

Mary Laier

Albert L Papp, Iii

Jwala Dinesh Gupta Chakka

Sanjeev K Mishra

In general, techniques are described for load balancing, with a service node, packet flows using stateless load balancing that adapts to server failure to provide flow affinity to initially selected servers for the duration of respective flows. In one example, service node device applies stateless load balancing to packet flows to distribute the flows among a plurality of servers. The service node determines a failure of a failed server and then receives an initial packet of a packet flow from the packet flows and forwards the initial packet to an active server. The service node generates a mapping of the packet flow to the active server, determines a recovery of the failed server, receives a subsequent packet of the packet flow, and forwards the subsequent packet of the packet flow to the active server based at least on the mapping of the packet flow to the active server.

Adaptive stateless load balancing

Methods, apparatuses and systems that dynamically adjust the selection of differentiated network services for selected classes of traffic or network applications in response to changing network conditions or application performance levels.

Adaptive, application-aware selection of differntiated network services

Methods, apparatuses and systems directed to an adaptive network traffic monitoring and measurement system. In one implementation, the present invention provides a measurement engine that monitors data flows on the packet processing path of a network device and executes measurement policy that control the granularity with which measurement data is recorded. In one implementation, the present invention provides a filtering and aggregation mechanism that can seamlessly adapt to changes in measurement policy configuration.

Adaptive, flow-based network traffic measurement and monitoring system

An exemplary embodiment provides for a method for recording network traffic data that can be used in a network device operably disposed in a network, wherein the network device comprises a measurement data structure including at least one entry and a corresponding measurement key and wherein the at least one entry comprises at least one measurement attribute. The method includes receiving a packet and matching the packet to a data flow. A measurement policy for the data flow is identified, wherein the measurement policy comprises one or more measurement axes corresponding to respective data flow attributes, and a rule set relating to at least one of the one or more measurement axes. The rule set is applied to dynamically select one or more of the measurement axes in the measurement policy to be applied to the data flow. A measurement key is created based on the selected one or more measurement axes and attributes of the data flow corresponding to the selected one or more measurement axes. The measurement key is matched to an entry in a measurement data structure. If a matching entry is found, at least one of the one or more measurement attributes is updated corresponding to the entry based on the received packet. If no matching entry is found, a new entry is created in the measurement data structure.

Method and system for dynamically capturing flow traffic data

An adaptive compression mechanism that dynamically selects compression algorithms applied to network application traffic to improve performance. One implementation includes an arbitration scheme that reduces the impact on computing resources required to analyze different compression algorithms for different network applications. The adaptive compression functionality of the present invention can be integrated into network application traffic management or acceleration systems.

Adaptive network traffic compression mechanism including dynamic selection of compression algorithms

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San Jose, CA, United States of America

Li Fang