Narus, Inc.

Cisco Technology, Inc.

Sprint Communications Comapny L.p.

Sprint Spectrum L.p.

Antonio Nucci

Ram Keralapura

Supranamaya Ranjan

Nina Taft

Mario Baldi

Sabyasachi Saha

Zhi-Li Zhang

Yong Liao

Alok Tongaonkar

Ruben Torres

Gyana Ranjan Dash

Aleksandar Kuzmanovic

Carlos M Pignataro

Stanislav Miskovic

Ahmed Khattab

Manjula Shivanna

Han See Song

Joshua Robinson

Christophe Diot

Marco Mellia

Donald Mark Allen

Sujit Biswas

Richard Montgomery Plane

Su Chen

Samudra Harapan Bekti

Kabeer Noorudeen

Tatiana Alexandrovna Gaponova

Konstantin Grechishchev

Qiang Ma

Sung-Ju Lee

Hui Zang

Marios Iliofotou

Frederic Giroire

Ignacio Bermudez Corrales

Han Hee Song

Augustin Soule

Hesham Mekky

Lei Liu

Saurabh Verma

Ionut Trestian

Jian Qiu

Duen-Uei D Chen

Jaykishan Anilkumar Pandya

Lixin Gao

Prasad Potipireddi

Amod Augustin

Alexander Sasha Stojanovic

Supratik Bhattacharyya

Ali Ebtekar

Qihong Shao

Alex Van Truong

Amedeo Sapio

Ignacio Bermudez

Chen-Nee Chuah

Dmitry Goloubew

Giovanni Vigna

Dragan Milosavljevic

Ning Xia

Bianca Schroeder

Qiang Xu

Luigi Grimaudo

Elena Baralis

Christopher Kruegel

Stefano Traverso

Ramakrishna Keralapura

Smriti Bhagat

Luca Invernizzi

Ping Pamela Tang

Jianpeng Xu

Athena Wong

Ranjan Supranamaya

John Oberon

Prakash Mandayam Comar

Fulvio Giovanni Ottavio Risso

Soon-Tee Teoh

Narendra Anand

Yihua Wu

Martin Beverley

Shamya Karumbaiah

Martino Trevisan

Pranjal Daga

Idilio Drago

Ravi K Papisetti

Anastasia Feygina

Arvind Narayanan

Danilo Giordano

Pengkui Luo

Syed M Hussain

In one example embodiment, a server that is in communication with a network that includes a plurality of network elements obtains, from the network, a service request record that includes sensitive information related to at least one of the plurality of network elements. The server parses the service request record to determine that the service request record includes a sequence of characters that is repeated in the service request record, and tags the sequence of characters as a particular sensitive information type. Based on the tagging, the server identically replaces the sequence of characters so as to preserve an internal consistency of the service request record. After identically replacing the sequence of characters, the server publishes the service request record for analysis without revealing the sequence of characters.

Automated data anonymization

This disclosure describes techniques for providing a network diagnostic system with on-premise node processing and cloud node processing to optimize bandwidth usage and decrease memory footprint. The on-premise node may receive streaming telemetry from connected network devices and encode to the telemetry data into filtered data objects. The on-premise node may determine whether the state of a network device has changed to determine to push the filtered data object to a cloud node for further diagnostic analysis. The cloud node may include a gateway and a pool of proxy servers, wherein each proxy server is designated to perform diagnostic analysis on a single product type.

Methods and apparatus for optimizing bandwidth consumption in support of intense network-wise health assessment

Techniques for receiving operational preferences for operating network devices, and determining software updates for the network devices based on the operational preferences. A recommendation system may determine a group of network devices in a device network based on the network devices in the group performing a common functional role or have common attributes. The recommendation engine may further receive the operational preferences for the group of network devices from a user associated with the device network. These operational preferences may be continuously, or periodically, evaluated against actual operating conditions of the group of network devices to determine whether a risk metric associated with the actual operation conditions violates an operational preference. In some instances, the recommendation system may provide the user with access to a recommendation to run updated software that is more optimized for the network device and that satisfies the operational preferences of the user.

Determining customized software recommendations for network devices

Sequences of computer network log entries indicative of a cause of an event described in a first type of entry are identified by training a long short-term memory (LSTM) neural network to detect computer network log entries of a first type. The network is characterized by a plurality of ordered cells F=(x, c, h) and a final sigmoid layer characterized by a weight vector w. A sequence of log entries xis received. An hfor each entry is determined using the trained F. A value of gating function G(h, h)=II (w(h&#x2212;h)+b) is determined for each entry. II is an indicator function, b is a bias parameter. A sub-sequence of xcorresponding to G(h, h)=1 is output as a sequence of entries indicative of a cause of an event described in a log entry of the first type.

Neural network-assisted computer network management

A Services Delivery Platform (SDP) architecture is provided that is configured to onboard new data sets into an SDP data lake. The SDP enables the crowdsourcing of data on-boarding by configuring this process into an interactive, intuitive, step-by-step guided workflow while governing/controlling key functions like verification, acceptance and execution.

Crowdsourcing data into a data lake

Presented herein are methods associated with a Services Delivery Platform (SDP) architecture for a distributed application building blocks, such as microservices, deployment-agnostic. The system includes a central compute node and numerous remote compute nodes. Techniques are provided to 'onboard' and assimilate the capabilities of remote compute nodes so that they are an integrated part of the SDP system and can be accessed and used in connection with one or more services provided by the SDP system.

Automatically and remotely on-board services delivery platform computing nodes

At a communication server, a first computer device and a second computer device are connected to a collaborative support session configured to support audio communications, screen sharing, and control of the first computer device by the second computer device. Screen sharing video image content is converted to a text sequence with timestamps. A text log with timestamps is generated from the text sequence. Using a command-based machine learning model, a sequence of commands and associated parameters, with timestamps, are determined from the text log. Audio is analyzed to produce speech-based information with timestamps. The command sequence is time-synchronized with the speech-based information based on the timestamps of the command sequence and the timestamps of the speech-based information. A knowledge report for the collaborative support session is generated. The knowledge report includes entries each including a timestamp, commands and associated parameters, and speech-based information that are time-synchronized to the timestamp.

Extracting knowledge from collaborative support sessions

A network monitor may receive network log events and identify: a first set of network devices that have reported a target network log event, a second set of network devices that have not reported the target network log event, a first set of network log events reported by the first set of network devices, and a second set of network log events reported by the second set of network devices. The network monitor may determine which network log events are legitimate, and filter the legitimate network log events from the first set of network log events or the second set of network log events to produce a group of suspicious network log events that may be correlated with the target network log event. The network monitor may predict future suspicious network log events that may be correlated with the target network log event in order to predict equipment failures.

Predicting computer network equipment failure

In one embodiment, a device receives sensor data from a plurality of nodes in a computer network. The device uses the sensor data and a graph that represents a topology of the nodes in the network as input to a graph convolutional neural network. The device provides an output of the graph convolutional neural network as input to a convolutional long short-term memory recurrent neural network. The device detects an anomaly in the computer network by comparing a reconstruction error associated with an output of the convolutional long short-term memory recurrent neural network to a defined threshold. The device initiates a mitigation action in the computer network for the detected anomaly.

Spatio-temporal anomaly detection in computer networks using graph convolutional recurrent neural networks (GCRNNs)

Systems, methods, and computer-readable media for identifying an optimal cluster configuration for performing a job in a remote cluster computing system. In some examples, one or more applications and a sample of a production load as part of a job for a remote cluster computing system is received. Different clusters of nodes are instantiated in the remote cluster computing system to form different cluster configurations. Multi-Linear regression models segmented into different load regions are trained by running at least a portion of the sample on the instantiated different clusters of nodes. Expected completion times of the production load across varying cluster configurations are identified using the multi-linear regression models. An optimal cluster configuration of the varying cluster configurations is determined for the job based on the identified expected completion times.

Cost-optimal cluster configuration analytics package

In one embodiment, a device in a network receives one or more data units. The device calculates a hash value based on the one or more data units and using a hash function. Ranges of hash values generated by the hash function are assigned to different devices along the path such that any given hash value generated by the hash function is assigned to a predefined number of the devices along the path. The device determines whether the calculated hash value is within the range of hash values assigned to the device. The device stores data derived from the one or more data units, when the device determines that the calculated hash value is within the range of hash values assigned to the device.

Distributed coordination of caching and processing by networking devices

Growing community of inventors

San Jose, CA, United States of America

Antonio Nucci