An application monitoring infrastructure that enables application configuration changes on multiple machines across multiple OS types to be tracked by identifying data containers that are to be monitored for changes, detecting a change to a monitored data container, and storing data representative of a changed version of the monitored data container responsive to detecting that the monitored container was changed. The data containers that are to be monitored for changes are identified from templates, and a unique template is provisioned for each of the applications.

A vehicle includes a controller area network (CAN) and a plurality of a controllers in communication with each other via the CAN, wherein each controller of the plurality of controllers is configured to time-stamp messages transmitted via the CAN using a vehicle-wide synchronized clock, determine a worst-case transmission delay via the CAN based on the time-stamps for messages received from other controllers of the plurality of controllers, and based on the worse-case transmission delay, set a dynamic recovery timer for a malfunctioning controller of the plurality of controllers to recover after its malfunction, wherein the dynamic recovery timer prevents a particular controller that was malfunctioning but has since recovered from being incorrectly designated as a malfunctioning controller in need of service.

The disclosure provides an approach for measuring performance between a virtualized desktop infrastructure (VDI) client running on a client device and a remote computing device. Embodiments include generating, by a performance client on the client device, an event and storing a time associated with generating the event. Embodiments include transmitting, by the VDI client to the remote computing device, a message based on the OS event. Embodiments include determining, by a performance agent on the remote computing device, a time associated with receiving the message at the remote computing device and causing an indication of the time to be displayed in a virtual desktop screen. Embodiments include extracting, by the performance client, from the virtual desktop screen, the time, and determining a performance metric based on the extracted time and the time associated with receiving the message at the remote computing device.

In one embodiment, an exemplary method includes receiving multi-dimension information from a data domain operating system running on the server; determining that multiple drive failures occurred within a predetermined time frame based on the multi-dimension information; and extracting a list of system-level events and a timestamp of each event from the multi-dimension information. The method further includes determining a list of components impacted by the list of the system-level events based on the list of system-level events and the timestamp of each event; and determining one or more system-level events associated with one or more impacted components as root causes of the multiple drive failures based on the multi-dimension information. The method uses information from multiple regions of the DAE and correlate the information using a predetermined algorithm to automatically more efficiently identify one or more possible root causes of the multiple drive failures.

A computer system is provided for retrospectively processing a data structure that includes a plurality of entries. The computer system determines if certain data transactions requests that have been recorded in the data structure could have been executed differently (e.g., by being processed at an earlier point in time). For a given entry in the data structure, the system determines if data transaction request could have at least partly succeeded against a prior recorded state of at least one of two ordered lists of pending data transaction requests. Another entry is then found that caused the initial entry to fail in execution and a time delta is stored between the timestamp of the another entry and the timestamp of the initial entry.

Systems and methods are disclosed for processing and executing queries against one or more dataset. As part of processing the query, the system determines whether the query is susceptible to a significantly imbalanced partition. In the event, the query is susceptible to an imbalanced partition, the system monitors the query and determines whether to perform a multi-partitioning determination to avoid a significantly imbalanced partition.

A distributed, transactional database uses timestamps, such as logical clock values, for entry versioning and transaction management in the database. To write to the database, a service requests a timestamp to be inserted into the database with a new version of data. During a backup procedure, a cleanup process is paused, issuing new timestamps is paused, and a backup timestamp is generated, which results in an effective backup copy. During a restore of a backup, a snapshot of the database is loaded and any entries older than the backup timestamp are deleted, which ensures that a consistent restore has occurred.

A computerized method of diagnosing a mislabeling of a source type of a received event. The method comprising operations of receiving an event by a source type analysis logic with a data index and query system, wherein the event includes a portion of raw machine data and is associated with a specific point in time, obtaining an original source type assigned to the event and one or more predicted source types. The one or more predicted source types are determined by analysis of a data representation of the event in view of training data and the training data includes a plurality of data representations corresponding to known source types. Additionally, the computerized method also includes an operation of, determining whether the event has been mislabeled and in response to determining the event has been mislabeled, diagnosing a source of the mislabeling.

The discussion relates to automatically providing information about what code sequences contribute to a length of time a program takes to execute. One example can collect context switch and ready thread event tracing data from a program over a period of interest and identify time blocks of program threads from the period of interest. The example can distinguish individual time blocks that contribute to execution time for the period of interest from other individual time blocks that do not contribute to the execution time.

Technologies are disclosed for the automated, rule-based generation of models from arbitrary, semi-structured observation data. Context data of received observation data, like data describing the location of on which a phenomenon was observed, is used to identify related observations, to generate entities in a model describing the observed data and to assign observations to model data. Mapping rules may be used for the on-demand generation of models, and different sets of mapping rules may be used to generate different models out of the same observation data for different purposes. Further, observation time data may be used to observer the temporal evolution of the generated model. Possible use cases of the so generated models include the interpretation of observation data that describes unexpected operation conditions in view of the generated model, or to determine how a monitored system reacts on changing conditions, like increased load.