Sap Se

Seshadri Chatterjee

Shashank Shekhar

Vikas Ganapati Hegde

Rajendra Kumar

Rahul Choudhary

Dinesh Rao

A machine learning model including an autoencoder may be trained based on training data that includes sequences of non-anomalous performance metrics from an information technology system but excludes sequences of anomalous performance metrics. The trained machine learning model may process a sequence of performance metrics from the information technology system by generating an encoded representation of the sequence of performance metrics and generating, based on the encoded representation, a reconstruction of the sequence of performance metrics. An occurrence of the anomaly at the information technology system may be detected based on a reconstruction error present in reconstruction of the sequence of performance metrics. Related systems, methods, and articles of manufacture are provided.

Variational autoencoding for anomaly detection

Methods and apparatus, including computer program products, are provided for optimizing information presentation. In one aspect, there is provided method, which may include receiving, by a monitoring engine, one or more messages sent from one or more runtime instances of a system being monitored by the monitoring engine; storing, by the monitoring engine, the received one or more messages; prioritizing, by the monitoring engine, the one or more runtime instances for presentation at a user interface view by determining, based on the received one or more messages, a rating for each of a plurality of categories associated with the one or more runtime instances; selecting, based on the prioritizing, information indicative of the state of the prioritized one or more runtime instances; generating a user interface view including the selected information; and displaying the generated user interface view. Related methods, systems, and articles of manufacture are also disclosed.

Optimal visualization of systems with large quantity of technical servicer instances

Various embodiments of systems, computer program products, and methods to provide alert management based on alert rankings are described herein. In an aspect, a plurality of alerts corresponding to a plurality of objects are received. A check is made to determine whether at least two alerts in the plurality of alerts include an identical priority. When the at least two alerts include the identical priority, the alert rankings for the at least two alerts are determined based on a runtime attribute. Further, when two or more alerts in the at least two alerts include the identical alert ranking upon determining the alert rankings based on the runtime attribute, the alert rankings of the two or more alerts are determined based on another runtime attribute. The plurality of alerts including the alert rankings are rendered to provide an order in which the plurality of alerts to be addressed.

Alert management based on alert rankings

Automatically setting a new threshold for a computer metric by detecting a first threshold for the computer metric, continuously taking measurements of the value of the computer metric at a predetermined rate, calculating the average of the measurements of the value of the computer metric over a predetermined time period, and calculating a second threshold for the computer metric based on the average of the values of the measurements. Calculating the second threshold may comprise establishing a base value for the threshold, establishing a maximum deviation of the threshold, and determining the average of the value of the measurements with respect to the base value and the maximum deviation.

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Kolkata, India

Seshadri Chatterjee