POLICY ENGINE WHICH SUPPORTS APPLICATION SPECIFIC PLUG-INS FOR ENFORCING POLICIES IN A FEEDBACK-BASED, ADAPTIVE DATA NETWORK

Patent №

US 6,505,244

Granted

2003-01-07

Filed 1999

Owner

CISCO TECHNOLOGY, INC.

Lab

AI components

2

kr · planning

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

09342337

A feedback-based adaptive network is described wherein at least a portion of the network elements report operating information relating to network conditions to a centralized data store. The information which is reported to the data store is analyzed by a policy engine which includes a plurality of application specific plug-in policies for analyzing selected information from the data store and for computing updated control information based upon the analysis of the information. The updated control information is fed back to selected network elements to thereby affect operation of the selected elements. Typically, when the operation of a network element has been affected, its corresponding operating information will change. The new or changed network element operating information is then reported to the data store and analyzed by the policy engine. The policy engine may then generate new or updated control information for affecting the operation of selected elements in the network. In this way, the dynamic and automatic feedback control of network elements is provided in order to allow the network to adapt to changing conditions. Events relating to changing conditions in the network may be reported to selected elements in the network using an event notification service. Additionally the adaptive, feedback-based network of the present invention may include a network quality monitoring system for evaluating performance characteristics or other aspects of the network based upon predetermined standards or criteria. If it is determined that a particular characteristic of the network does not conform with the standards established for that characteristic, the policy which controls that particular characteristic of the network may be automatically and dynamically modified to thereby affect the network performance.

Knowledge representationPlanningH04L 47/822H04L 47/11H04L 47/20H04L 47/29H04L 47/32H04L 47/70H04L 47/762H04L 47/765+2 more

AI classification

Planning1.00
Knowledge representation0.82
AI hardware0.03
Natural language0.01
Machine learning0.00
Vision0.00
Evolutionary computation0.00
Speech0.00

Ownership

CISCO TECHNOLOGY, INC.

assignment · 101550250

Assignors

NATARAJAN, SHANKAR, HARVEY, ANDREW G., LEE, HSUAN-CHUNG, RAWAT, VIPIN, PEREIRA, LEO

On an employer assignment, the assignors are typically the inventors.

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