Cisco Technology, Inc.

Prasenjeet Acharjee

Lavan Kumar Peechara

Stefano Piciaccia

Alberto Arnaldo Tanzi

Giacomo Boracchi

Luca Magri

Pietro Invernizzi

Enrico Sozio

Bhavadharini Krishnamoorthy

Sai Kishore Bhyri

Subramanian Karunanithi

Togis Thomas

Antonino Maria Rizzo

Cesare Alippi

In some aspects, the techniques described herein relate to a method that includes: obtaining optical channel spectrum data that includes amplified spontaneous emission data and channel data associated with optical signals propagated through an optical fiber; fitting an amplified spontaneous emission trend to the amplified spontaneous emission data; fitting a channel trend to the channel data; jointly optimizing the amplified spontaneous emission trend and the channel trend to determine an optimized channel trend; and determining an anomaly in the channel data based upon the optimized channel trend.

Automatic trend identification and anomaly detection in optical channel monitoring spectrum

A network node in an optical network dynamically generates a routing table based on attributes of the optical network. The network node obtains attributes characterizing the optical network, which includes multiple network nodes connected by optical links. The network node calculates cost values for sending data from the network node to one or more next hop nodes that are connected to the network node. Each particular cost value is associated with a probability of success of sending the data to a particular next hop node based on a particular permutation of the attributes characterizing the optical network. The network node generates a routing table correlating the permutations of the attributes with each next hop node based on the cost values.

Route selection in optical networks based on machine learning

Anomaly detection and filtering based on system logs is provided via receiving, at a first time, a first sequence of log entries from a networked system; generating, based on previously observed log entries including the first sequence of log entries, predicted log entries for a second time; receiving, at the second time, a second sequence of log entries from the networked system; determining whether the second sequence is anomalous based on comparing the second sequence with the predicted log entries; in response to determining that the second sequence is anomalous, determining whether the second sequence is noteworthy based on a function entropy of the first sequence and a sentiment polarity of the second sequence; and in response to determining that the second sequence is noteworthy, generating an anomaly report that includes the second sequence and a root cause.

Anomaly detection and filtering based on system logs

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Bengaluru, India

Prasenjeet Acharjee