Mellanox Technologies, Ltd.

Aristotle University of Thessaloniki

Nikolaos Argyris

Elad Mentovich

Paraskevas Bakopoulos

Ioannis (Giannis) Patronas

Dimitrios Syrivelis

Dimitrios Kalavrouziotis

Tamar Viclizki Cohen

Eitan Zahavi

Louis Bennie Capps, Jr

Avraham Ganor

Vadim Gechman

Nimer Hazin

Prethvi Ramesh Kashinkunti

Julie Irene Marcelle Bernauer

Nikos Pleros

Konstantinos Tokas

Systems, computer program products, and methods are described herein for machine learning (ML) based network resilience and steering. An example system monitors data traffic across one or more network ports and determines a first data traffic pattern from the data traffic. The system further determines, via a ML subsystem, that the first data traffic pattern is indicative of a security threat to a first network port. In response to determining that the first data traffic pattern is indicative of the security threat to the first network port, the system further isolates the first network port from the one or more network ports.

Machine learning (ML) based systems for air gapping network ports

Systems, computer program products, and methods are described herein for network discovery, port identification, and/or identifying fiber link failures in an optical network, in accordance with an embodiment of the invention. The present invention may be configured to sequentially connect each port of an optical switch to a network port of a server and generate, based on information associated with network devices connected to the ports, a network map. The network map may identify which network devices are connected to which ports of the optical switch and may permit dynamic port mapping for network installation, upgrades, repairs, and/or the like. The present invention may also be configured to determine a fiber link in which a failure occurred and reconfigure the optical switch to allow communication between an optical time-domain reflectometer and the fiber link to test the fiber link.

Systems and methods for identifying fiber link failures in an optical network

A transceiver module for providing operational resilience is presented. The transceiver module is configured to receive first data via a first optical module in a first configuration of operation and detect, using an adapter that is operationally connected to the first optical module, an operational failure of the first optical module. In response to detecting the operational failure, the transceiver module is configured to switch, via the adapter, from the first configuration of operation to a second configuration of operation by: automatically engaging a second optical module; triggering the first data that was initially directed into a first input port of the first optical module to be directed into a second input port of the second optical module; and receiving the first data from a second output port of the second optical module.

Transceiver module

Systems, computer program products, and methods are described herein for machine learning (ML) based system for network resilience and steering. An example system monitors data movement across one or more network ports; extracts network performance indicators associated with the data movement; determines, via a machine learning (ML) subsystem, that a status of a first network port is indicative of operational failure based on at least the network performance indicators; determines that the first network port is associated with a first network port cluster; determines a redundant network port and an intermediate network switch associated with the first network port cluster; and triggers the intermediate network switch to reroute a portion of network traffic from the first network port to the redundant network port in response to the status of the first network port.

System for machine learning (ML) based network resilience and steering

A beamforming element comprises an imprinting-shifting component configured to imprint an input signal onto a second beam to form an imprinted beam and adjust the optical phase of the imprinted beam; one or more multi-beam optical couplers configured to receive a phase-shifted imprinted beam and a first beam and form an interference beam from the combination thereof; and one or more optical-to-electrical converter components configured to receive an interference beam and generate an electrical signal based thereon that includes the beamforming time delay(s) and is frequency up/down-converted with respect to the input signal.

Beamforming systems, networks, and elements configured for simultaneous optical up/down conversion and beamforming

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Zografou, Greece

Nikolaos Argyris