A service monitoring system executing on one or more processors may have operations that are determined by control information. Control over the operation of the service monitoring system can be exerted through the use of a graphical interface. The graphical interface may present the control information of a new or existing correlation search definition for user interaction. The service monitoring system may maintain a data store of key performance indicator (KPI) data, where a KPI value in the data store is produced by a KPI-defining search query that derives the value from machine data associated with one or more entities that perform a monitored service. A correlation search definition of the service monitoring system determines how a search of the KPI data is conducted, how its data is evaluated to determine whether a triggering condition has been met, and, if so, determines what triggered action is to be initiated.

Aspects of the disclosure describe methods and systems for cross-referencing forensic snapshots over time. In one exemplary aspect, a method may comprise receiving a first snapshot of a computing device at a first time and a second snapshot of the computing device at a second time and applying a pre-defined filter to the first snapshot and the second snapshot, wherein the pre-defined filter includes a list of files that are to be extracted from each snapshot. The method may comprise subsequent to applying the pre-defined filter, identifying differences in the list of files extracted from the first snapshot and the second snapshot. The method may comprise creating a change map for the computing device that comprises the differences in the list of files over a period of time, wherein the period of time comprises the first time and the second time, and outputting the change map in a user interface.

Techniques performed by a data processing system for diagnosing problems with a communications platform include obtaining query parameters including an aggregation operator for invoking a machine learning algorithm configured to analyze performance data for the communications platform, automatically executing the query on the performance data to obtain query results by invoking the machine learning algorithm on the performance data to automatically identify a plurality of clusters of data indicative of a performance problem, and presenting a visualization of the query results. The visualization includes indicators identifying cluster properties for which the query results are further refinable and one or more second indicators identifying the second subset of the second cluster properties which are not relevant for further refining the first query results. The indicators are actuatable to automatically update and re-execute the first query based on the respective indicator that is actuated.

A memory evaluation method and apparatus are provided. The method includes: determining a health degree evaluation model indicating a relationship in which a health degree of a memory changes with at least one health degree influencing factor of the memory; obtaining at least one running parameter value corresponding to each of the at least one health degree influencing factor; separately matching the at least one running parameter value corresponding to each health degree influencing factor to the health degree evaluation model, to obtain the health degree of the memory; and outputting health degree indication information which indicate whether the memory needs to be replaced. Therefore, the memory is not faulty and the health degree of the memory is a relatively low, a user is prompted to replace the memory.

A system includes a host and a display. The host includes a programmable logic device (PIP), a baseboard management controller (BMC) and a switch. The PLD performs a power-on procedure based on a power-on sequence code, generates variable character information in the power-on procedure, and fills the variable character information into a variable field in a preset log text file to result in an updated log text file. When it is determined that the power-on procedure is not normally completed, the PLD controls the switch to switch to a debug mode, and transmits a video signal containing debug information corresponding to the updated log text file to the switch so that the video signal is outputted to the display.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for monitoring performance of computing systems. In some implementations, a set of tasks for a server system to perform is identified. Multiple performance testing cycles are performed, in which each of the performance testing cycles includes: sending, for each task in the set of tasks, a request for the server system to perform the task; receiving a response from the server system for each of the requests, and storing a performance measure for each of the tasks based on the response received from the server system for the task. Based on the performance measures for the multiple performance testing cycles, an evaluation is performed whether conditions are satisfied for adjusting one or more operating parameters of the server system or for providing a notification regarding the operation of the server system.

In one aspect, a system receives a request to cluster a set of log records. Responsive to receiving the request, the system identifies at least one dictionary that defines a set of tokens and corresponding token weights and generates, based at least in part on the set of tokens and corresponding token weights, a set of clusters such that each cluster in the set of clusters represents a unique combination of two or more tokens from the dictionary and groups a subset of log records mapped to the unique combination of two or more tokens. The system may then perform one or more automated actions based on at least one cluster in the set of clusters.

Views may be generated for bias metrics or feature attribution captured in machine learning pipelines. A request to create a view of bias metrics or feature attribution may be received. The bias metrics or feature attribution may have been determined in a machine learning pipeline as part of executing a training job that specified the bias metrics or the feature attribution. A development application may access a data store that stores the bias metrics or the feature attribution determined in the machine learning pipeline. A view based on the bias metrics or feature attribution may be generated and provided.

A method of performing error analysis in a system comprising microservices comprises identifying a root cause error span from among a plurality of error spans for a trace, wherein an error span is a span that returns an error to a microservice that generates the span, and wherein a root cause error span is an error span associated with an error originating microservice. The method further comprises determining a call path associated with the root cause error span, where the call path comprises a chain of spans starting at the root cause error span, and where each subsequent span in the chain is a parent span of a prior span. Subsequently the method comprises mapping each span in the chain to a span error frame to create an error stack and rendering an image of the error stack.

Systems, methods, computer-readable media, and apparatuses for identifying and executing one or more interactive condition evaluation tests to generate an output are provided. In some examples, user information may be received by a system and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. A user interface may be generated including instructions for executing the identified tests. Upon initiating a test, data may be collected from one or more sensors in the computing device. The data collected may be transmitted to the system and may be processed using one or more machine learning datasets to generate an output.