Citibank, N.a.

Citigroup Technology, Inc.

Sunny Behl

Sandeep Ravi

Suresh Kanyaka Billa

Hariharan Badrinathan

Presented herein are system and methods for countermeasures to address anomalies in microservices. A server having one or more processors coupled with memory may receive a first plurality of metrics from a defined set of microservices for a function. The server may apply the first plurality of metrics to an ensemble of anomaly detection models to generate a plurality of classifications. Each classification may indicate the first plurality of metrics as one of anomalous or normal from a respective model of the ensemble of anomaly detection models. The server may identify a majority of the plurality of classifications as corresponding to an anomaly event. The server may determine, responsive to identifying the majority, that at least one of the first plurality of metrics satisfies a criterion of a policy of a plurality of policies. The server may perform a countermeasure identified by the policy to address the anomaly event.

Microservices anomaly detection

Presented herein are systems and methods for managing networked environments. A computer system may provide a user interface for configuring a plurality of groups of servers each hosting a resource for an application. The user interface may include: a first element configured to select, upon interaction, at least one group of servers from the plurality of groups of servers to which to install a patch for the application; a second element configured to identify, upon interaction, a first group of servers of the plurality of groups of servers to which to transfer network traffic associated with the application and communicated with a second group of servers of the plurality of groups of servers; and a third element configured to provide, upon interaction, one or more performance indicators for at least one of a plurality of functions of the application.

Platform for automated management of servers

Presented herein are system and methods for detecting anomalies in microservices. A server may receive a first plurality of metrics from a microservice of a plurality of microservices. Each of the microservices may provide a respective function for an application independently from other microservices. The server may apply the first plurality of metrics to an ensemble of anomaly detection models to generate classifications. Each of the classifications may indicate the first plurality of metrics as one of anomalous or normal from a respective model of the ensemble. The ensemble may be trained using a second plurality of metrics from the microservice. The server may identify a majority of the plurality of classifications as indicating the first plurality of metrics as anomalous. The server may determine that at least one of the first plurality of metrics satisfies a threshold. The server may generate an alert to indicate an anomaly event.

Growing community of inventors

Irving, TX, United States of America

Sunny Behl