A method for detecting an anomaly includes retrieving a log file that includes log entries, grouping the log entries into clusters of log entry types based on number of occurrences and average time interval, and discovering a sequence of the log entry types within each of the clusters. The sequence of the log entry types is based on a shortest path from a first one of the log entry types to a last one of the log entry types.

A system and method for determining automated uptime of internet-based software may include one or more memories storing check suites, each check suite including an ordered sequence of a plurality of checks corresponding to an execution flow of an ordered sequence of functions of the internet-based software; and one or more processors configured to determine a total amount of down time for a monitoring period by: during each time interval of the monitoring period, performing each of the plurality of check suites on the internet-based software, and applying at least one weighing factor to results of performing each of the plurality of check suites.

An index associates fingerprints of file segments to container numbers of containers within which the file segments are stored. At a start of migration, a boundary is created identifying a current container number. At least a subset of file segments at a source storage tier are packed into a new container to be written to a destination storage tier. A new container number is generated for the new container. The index is updated to associate fingerprints of the at least subset of file segments to the new container number. A request is received to read a file segment. The index is queried with a fingerprint of the file segment to determine whether the request should be directed to the source or destination storage tier based on a container number of a container within which the file segment is stored.

Described are computer-implementable method, system and computer-readable storage medium for supervised graph-based model for prediction of program failure using program log files. Using log file from a running program application, a log file graph is created. Node-level labels are adding to the log file graph, where the labels include an indication of first failure. The node-level labeled log file graph is processed by a graph neural network (GNN) and predictions are provided as to program cause of failure or first failure indication of other log file graphs based on the GNN processed node-level labeled log file graph.

Techniques described herein can monitor various data metrics. The auto-insight techniques can further detect and rank data segments that contributed to, or counteracted, shifts in data and detect when such shifts occurred. Thus, the techniques described herein can detect and identify root causes in shifts in different metrics. The techniques include pruning and ranking causes to identify the root causes and identify non-relevant factors, as well.

Systems and methods for managing the degradation of IHS performance due to software installations are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: identify a workload; in response to the identification, configure a performance measurement; calculate a level of performance degradation of the IHS based, at least in part, upon the performance measurement; and in response to the level of performance degradation meeting a threshold value, provide an indication to a user or Information Technology (IT) administrator.

The disclosed embodiments relate to logging activities of memory devices and adjusting the operation of a controller based on the activities. In one embodiment, a method comprises monitoring, by a memory device, die temperatures and data sizes of commands issued to the memory device; determining, by the memory device, a target size for a buffer based on the die temperatures and data sizes; and adjusting, by the memory device, a current size of the buffer to meet the target size.

A data quality assessment and monitoring tool addresses inconsistency in large data sets from differing sources, determining data quality attributes such as completeness, conformity, validity, and accuracy. Flexible taxonomies and rollup strategies accommodate diverse business unit needs across a complex enterprise, and provides insight into individual entities' performance. An exemplary tool comprises a data importer for importing data from a data lake; a rules manager for generating rules and rule sets; a scoring engine for generating data quality scores; a job manager; a data profiler for running data assessment tasks and collating the data quality scores for a plurality of hierarchical data entity units; a hierarchical scoring aggregator for aggregating sets of data quality scores into a plurality of first tier aggregate data quality scores and to further aggregate the first tier aggregate data quality scores into one or more second tier aggregate data quality scores; and a reporting component.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for improving distributed monitoring using different groups of remote devices. In some implementations, a system communicates with a set of remote devices involved in a first monitoring program that involves collection of data from the remote devices over a communication network. The system identifies a pattern or similarity among monitoring data collected from a subset of the remote devices involved in the first monitoring program. The system determines that the identified pattern or similarity satisfies one or more criteria for initiating additional monitoring. In response, the system determines parameters specifying second types of data to collect in a second monitoring program. The system configures one or more devices to perform monitoring for the second monitoring program including acquiring data for the second types of data and providing the acquired data to a server over the communication network.

An information handling system includes location sensor circuitry and a processor. The location sensor circuitry determines location data for the information handling system. The processor receives a unique session tag from a cloud server. The unique session tag is utilized to identify individuals associated with an event. The processor also provides the location data for the information handling system and the unique session tag to the cloud server. The processor receives one or more proposed destination points for the event from the cloud server. The processor receives a selection of one of the proposed destination points, and provides the selected destination point to the individuals associated with the event.