Nokia Solutions and Networks Oy

Nokia Technologies Oy

Alcatel Lucent

Tv Lakshman

Muralidharan Sampath Kodialam

Sarit Mukherjee

Fang Hao

Adiseshu Hari

Krishna P Puttaswamy Naga

Hyunseok Chang

Mostafa Uddin

Gordon Wilfong

In some embodiments, there may be provided a method that includes receiving a first traffic matrix; receiving information regarding links associated with each segment of the network; determining a total amount of segment flow using the at least one non-linear deflection parameter applied to the traffic demand of the first traffic matrix; determining a link flow for each of the links using the total amount of segment flow and the second input to the machine learning model; determining link utilization for each of the links using the link flows and a capacity for each of the links; learning, by the machine learning model using a gradient descent, a minimum of a maximum amount of the link utilization over the links by at least adjusting a value of the at least one non-linear deflection parameter. Related systems, methods, and articles of manufacture are also disclosed.

Machine learning segment routing for multiple traffic matrices

Various example embodiments for supporting multicast are presented herein. Various example embodiments for supporting stateless multicast may be configured to support stateless multicasting of packets based on encoding of multicast path information within the packets. Various example embodiments for supporting stateless multicasting of packets based on encoding of multicast path information within the packets may be configured to use a recursive scheme of encoding the multicast path information within the packets such that the header that arrives at each node of the multicast path informs the node of the outgoing links over which the packet is to be sent and the headers to be sent on the outgoing links (e.g., each node of the multicast tree is able to recognize each of its outgoing links from the received header and send the appropriate subtree encoding down each of its outgoing links).

Stateless and secure packet forwarding

In some embodiments, there may be provided a method that includes receiving, as a first input to a machine learning model, a measured link load; receiving, as a second input to the machine learning model, information indicating a network topology; receiving, as a third input to the machine learning model, at least one deflection parameter; learning, by the machine learning model, a first output to provide at least one updated deflection parameter, wherein the at least one updated deflection parameter indicates the fractional amount of traffic that is to be carried between the source node and the destination node and deflected through the intermediate node; and learning, by the machine learning model, a second output to provide dual variables that serve as a surrogate for a traffic matrix that could have generated the measured link load that is measured for the link of the network.

Telemetry-based segment routing using machine learning

System, method, and software for managing packet forwarding by end devices that use Zigbee protocol. In one embodiment, a P4 switch is implemented at an edge of an IP network as an intermediary between the end devices that use Zigbee protocol so that packets forwarded by each of the end devices flow through the switch. The switch includes a transceiver configured to receive a packet forwarded by a first endpoint in an end device and destined for a second endpoint. The switch includes a P4 parser configured to parse the packet to identify fields of the packet, and a P4 analyzer configured to process a plurality of match-action tables and the fields of the packet to provide data plane handling of the packet.

Service automation for IoT devices using P4

Apparatuses, methods and articles of manufacture for performing data leakage prevention are provided. Data leakage prevention may be performed by determining a signature of a transmitted document, the transmitted document being in transit to a location beyond a network boundary. The signature of the transmitted document is compared with one or more signatures of documents authorized to be transmitted beyond the network boundary. The transmitted document is prevented from being transmitted beyond the network boundary if the signature of the document does not correspond to a signature of a document authorized to be transmitted beyond the network boundary.

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Morganville, NJ, United States of America

Tv Lakshman