Sap Se

Tim Breitenbach

Patrick Jahnke

Luke Gain

Lauritz Rasbach

Bartosz Wilkusz

Embodiments of the present disclosure include techniques for predictive memory maintenance. In one embodiment, locations of correctable errors in a memory are observed. A machine learning (ML) system may be trained with patterns of correctable errors that result in uncorrectable errors. A trained ML monitors correctable errors to predict when memory requires maintenance. In another embodiment, error rates from multiple memories are monitored to predict memory channel and other upstream device failures.

Systems and methods for predictive memory maintenance

Systems and Methods for Automatically Detecting Root Cause Failures Described herein are techniques for automatically detecting root cause failures in a computing environment. A data center may experience a critical system failure that renders the data center inoperable. An administrator may utilize a root cause detector to analyze alerts generated from the data center to automatically detect the root cause of the critical system failure. Once detected, the administrator may investigate the device with the root cause failure to repair the data center. In some examples, the root cause detector may compare alerts received from the data center with root cause failures in a failure repository that it is familiar with to determine whether the present sequence of alerts is similar to alert patterns it has seen before. The root cause detector may be implemented with a classifier model, a large language model, a rule-based heuristics identifying spurious alert patterns, or combinations of these techniques.

Systems and methods for automatically detecting root cause failures

Root causes of network anomalies can be identified as follows. A subset of network entities that have experienced network anomalies during a time period are determined based on historical network data. A set of root cause candidates are selected among the plurality of network entities by iterating through the network topology, each root cause candidate being directly upstream of two or more network entities in the subset of network entities that have experienced network anomalies according to the network topology. Network entities that are root causes of the network anomalies are identified by removing root cause candidates that have a common upstream network entity that is also a root cause candidate from the set of root cause candidates leaving a set of remaining root cause candidates that are the root causes.

Identifying root causes of network anomalies

Embodiments of the present disclosure include techniques for predictive memory maintenance. In one embodiment, error locations in a RAM are specified by columns and rows. Error locations are detected and stored in a storage system. One or more plots of the error locations may be presented to a user. In some embodiments, the error locations are time stamped. Rules may be defined to automatically detect patterns of error locations statically or over time. Alerts may be generated automatically to perform maintenance of a computer system with failing memory.

Growing community of inventors

Munich, Georgia

Tim Breitenbach