Invention Grant
- Patent Title: Efficient machine learning for network optimization
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Application No.: US16170218Application Date: 2018-10-25
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Publication No.: US10666547B2Publication Date: 2020-05-26
- Inventor: David Sanchez Charles , Giorgio Stampa , Victor Muntés-Mulero , Marta Arias
- Applicant: CA, Inc.
- Applicant Address: US NY New York
- Assignee: CA, Inc.
- Current Assignee: CA, Inc.
- Current Assignee Address: US NY New York
- Agency: Foley & Lardner LLP
- Main IPC: H04L12/751
- IPC: H04L12/751 ; H04L12/755 ; H04L12/721 ; H04L12/24 ; H04L12/707 ; H04L12/715

Abstract:
An autonomous controller for SDN, virtual, and/or physical networks can be used to optimize a network automatically and determine new optimizations as a network scales. The controller trains models that can determine in real-time the optimal path for the flow of data from node A to B in an arbitrary network. The controller processes a network topology to determine relative importance of nodes in the network. The controller reduces a search space for a machine learning model by selecting pivotal nodes based on the determined relative importance. When a demand to transfer traffic between two hosts is detected, the controller utilizes an AI model to determine one or more of the pivotal nodes to be used in routing the traffic between the two hosts. The controller determines a path between the two hosts which comprises the selected pivotal nodes and deploys a routing configuration for the path to the network.
Public/Granted literature
- US20200136957A1 EFFICIENT MACHINE LEARNING FOR NETWORK OPTIMIZATION Public/Granted day:2020-04-30
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