Dell Products Inc.

Sankunny Jayaprasad

Cyril Jose

Sankara Rao Gara

Jon Robert Hass

Ramakanth Kanagovi

Guhesh Swaminathan

Choudary Maddukuri

Saheli Saha

Jason Fay

Sailaja Mahendrakar

Araiz Abdul Baqi

Michael Emery Brown

Mahesh Babu Ramaiah

One example method includes acquiring data from a knowledge base that includes message codes indicating conditions that occurred during performance of one or more test runs of a computing operation, and the message codes are included in message code sequences, processing the data by mapping the message codes, and message codes included in a customer issue log, to codes that are readable by a machine learning process, transforming the data to generate an output that comprises, for each message code sequence, relationships between each of the message codes in that message code sequence, extracting features from the transformed data, and the extracting generates multiple datasets that include the features, performing a similarity comparison by comparing a customer field issue with the datasets, and based on the similarity comparison, identifying and recommending a solution to the customer field issue.

Machine learning method to rediscover failure scenario by comparing customer's server incident logs with internal test case logs

Embodiments provide unique identification of telemetry reports generated by components of an IHS (Information Handling System) that supports a plurality of metric data sources. A source of metric data is detected, where the source may be a fixed or replaceable IHS component. The metric source is identified within a device descriptor table maintained by a remote access controller of the IHS. Based on a unique user-friendly label that is associated with the metric data source in the device descriptor table, the metric source is configured to generate metric reports. The generated metric reports are received and the label provided by the metric source is used to store data from the metric reports in a database row using the label as a unique database key. The user-friendly label can be used to perform efficient database queries without using a separate database column for storing a user-friendly description of the metric source.

Unique identification of metric values in telemetry reports

Telemetry system metric data that is collected within a data center may be collectively analyzed in order to identify zones within the data center that exhibit deviations in reported metric data. The analyzed metric data is collected from IHSs (Information Handling Systems), such as servers, operating in the data center. The metric data reported by the respective IHSs identifies the location the IHS within the data center. The collected metric data is analyzed to identify metrics that are correlated with locations within the data center. Within the identified metric data that is correlated with data center locations, a zone of the data center is identified that includes a subset of IHSs that have reported anomalous metric data relative to the metric data reported by neighboring IHSs. For instance, such a zone may be an aisle of the data center, one or more racks, and/or rows of servers spanning multiple racks.

Telemetry system supporting identification of data center zones

Systems and methods utilize telemetry data to provide administrators with metric information related to a detected IHS (Information Handling System) event, such as an error condition, where the provided metric information is particularized to the context of the event. A remote access controller (RAC) of the IHS stores metric reports received from metric sources. The RAC receives an indication of the event that specifies a first IHS component as a source of the event and specifies a time associated with the event. The RAC identifies stored metric reports generated by the first component prior to the first time and identifies stored metric reports generated by components that are logically and/o physically related to the first component. The RAC generates an event report that includes the metric reports generated by the first component prior to the first time and the metric reports generated by components related to the first component.

Event-based generation of context-aware telemetry reports

Techniques described herein relate to a method for performing testing operations for information handling systems. The method includes obtaining a test case from an information handling system; in response to obtaining the test case: obtaining log information associated with the test case from the information handling system; performing data preparation to generate processed subsequences using the log information; applying a plurality of prediction models to the processed subsequences and training data to generate anomalous subsequence predictions; generating ensemble anomaly scores and severity indexes associated with the processed subsequences using the anomalous subsequence predictions and the processed subsequences; making a determination that the ensemble anomaly scores and severity indexes associated with the processed subsequences result in detection of an anomalous subsequence; and in response to the determination: determining a next best test case associated with the anomalous subsequence; and initiating performance of the next best test case.

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Round Rock, TX, United States of America

Sankunny Jayaprasad