Cisco Technology, Inc.

Ca, Inc.

Computer Associates Think, Inc.

Donald Mark Allen

Dmitri Goloubev

Antonio Nucci

Gyana Ranjan Dash

Kabeer Noorudeen

Tatiana Alexandrovna Gaponova

Konstantin Grechishchev

David Delano Ward

Hugo Latapie

Enzo Fenoglio

James D Engquist

Jerry R Jackson

Mark W Emeis

Sawsen Rezig

Raphaël Wouters

Randall L Murrish

Didier Colens

Peter De Vriendt

Luc De Ghein

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

Presented herein are techniques to analyze network traffic and equipment based on telemetry generated by a plurality of network devices. A method includes generating first telemetry at a first network device, receiving, at the first network device, via an Internet Protocol anycast addressing scheme, at least one of second telemetry generated at a second network device, and third telemetry generated at a third network device, performing, on the first network device using a local processing unit, first analytics on the first telemetry, performing, on the first network device using the local processing unit, second analytics on the at least one of the second telemetry and the third telemetry, and transmitting data resulting from the first analytics and the second analytics to a fourth network device.

Distributed virtualization of telemetry processing with IP anycast

In one embodiment, a service that monitors a network detects a plurality of anomalies in the network. The service uses data regarding the detected anomalies as input to one or more machine learning models. The service maps, using a conceptual space, outputs of the one or more machine learning models to symbols. The service applies a symbolic reasoning engine to the symbols, to rank the anomalies. The service sends an alert for a particular one of the detected anomalies to a user interface, based on its corresponding rank.

Deep fusion reasoning engine (DFRE) for prioritizing network monitoring alerts

A digitized Intellectual Capital (IC) system obtains code modules configured to detect one or more issues in a computing system. The IC system selects from the code modules to generate a first set of code modules based on a corresponding value metric. The corresponding value metric for each code module in the first set of code modules is higher than a predetermined threshold. The IC system also samples from the remainder of the code modules unselected for the first set of code modules to generate a second set of code modules. The IC system runs the first set of code modules and the second set of code modules to detect the one or more issues and updates the corresponding value metric for at least one code module.

Predicting the efficacy of issues detected with machine executed digitized intellectual capital

In general, the techniques of this invention are directed to autonomic management of autonomic management systems. In particular, the embodiments of this invention use a measure, analyze, and respond model to autonomically manage one or more autonomic management systems. By understanding specific state information of these autonomic management systems, embodiments of the invention may achieve target performance for the autonomic management systems through operations monitoring, analyzing current system state against target state, and modifying the configurations or resources of the autonomic management systems.

Autonomic management of autonomous management systems

In general, the techniques of this invention are directed to determining whether a component failure in a distributed computing system is genuine. In particular, embodiments of this invention analyze monitoring data from other application nodes in a distributed computing system to determine whether the component failure is genuine. If the component failure is not genuine, the embodiments may adjust a fault tolerance parameter that caused the component failure to be perceived.

Autonomous system state tolerance adjustment for autonomous management systems

Growing community of inventors

Colorado Springs, CO, United States of America

Donald Mark Allen