A system, process, and computer-readable medium for configuring agents for monitoring deployed applications is described. A system, process, and computer-readable medium for configuring monitoring user interfaces, e.g., monitoring dashboards, that use information made available from the agents is also described. Through using application data available during creation of the agents, the agents may be configured using the user interface as modified by selections and displaying subsequent choices from the received application data. Using knowledge of the generated agents, monitoring dashboards may be generated via developers interacting with a user interface providing a list of available metrics accessible by the generated agents. Using the one or more user interfaces, developers may generate agents and/or monitoring dashboards with greater efficiency.

A method of managing components in a processing environment is provided. The method includes monitoring (i) a status of each of one or more computing devices, (ii) a status of each of one or more applications, each application hosted by at least one of the computing devices, and (iii) a status of each of one or more jobs, each job associated with at least one of the applications; determining that one of the status of one of the computing devices, the status of one of the applications, and the status of one of the jobs is indicative of a performance issue associated with the corresponding computing device, application, or job, the determination being made based on a comparison of a performance of the computing device, application, or job and at least one predetermined criterion; and enabling an action to be performed associated with the performance issue.

A multivariate time series model such as a Vector Auto Regression (VAR) model is built using fabric utilization, disk utilization, and CPU utilization time series data. The VAR model leverages interdependencies between multiple time-dependent variables to predict the start and length of an aperiodic backup time window, and to cause backup operations to occur during the aperiodic backup time window to thereby exploit the aperiodic backup time window for use in connection with backup operations. By automatically starting backup operations during predicted aperiodic backup time windows where the CPU, disk, and fabric utilization values are predicted to be low, it is possible to implement backup operations during time windows where the backup operations are less likely to interfere with primary application workloads, or system application workloads that need to be implemented to maintain optimal operation of the storage system.

A system and method for providing intelligence of software applications. The method comprises collecting, from a plurality of resources, data utilized to at least execute a plurality software applications of an organization; analyzing the collected data to identify the plurality of software applications; determining, by using a static analysis process, dependencies between the software applications of the identified plurality of software applications; compiling visibility data based on the identified plurality of software applications and the determined dependencies; generating a dataflow map, wherein the dataflow map includes the visibility data; and enabling a control of at least one policy based on the visibility data included in the dataflow map.

Disclosed are methods and systems for inactivity-based prompting of task completion. For instance, a task related to an account may be identified as an incomplete task along with a task status related to the task as incomplete. The task and task status may be stored in a data store in association with an account identifier. In response to receiving a session inactivity indication for an account session established via an application executing on a user computing device, the data store may be queried using the account identifier to determine the task status for the task. The task status may be determined as remaining incomplete, and a notification indicating the incompletion may be generated. Additionally, a communication channel may be determined for transmission of the notification that increases a likelihood of notification engagement to prompt completion of the task. The notification may be transmitted via the communication channel.

Systems, methods, and storage media are provided that are useful in a computing environment for receiving, modifying, and transforming service level information from database servers and entities in a hosted database environment. Multiple application programming interface (API) calls are made by a database observation system to request information for multiple service level indicators from database servers belonging to multiple different entities. Database observation system receives and aggregates the information for multiple service level indicators from each of the database servers belonging to multiple different entities. The database observation system provides, within a dashboard interface, the aggregated information for each of the multiple service level indicators, individual service level indicator scores, and aggregated service level indicator scores for each of the database servers for each of the multiple entities.

Techniques described herein relate to systems and methods of data storage, and more particularly to providing layering of file system functionality on an object interface. In certain embodiments, file system functionality may be layered on cloud object interfaces to provide cloud-based storage while allowing for functionality expected from a legacy applications. For instance, POSIX interfaces and semantics may be layered on cloud-based storage, while providing access to data in a manner consistent with file-based access with data organization in name hierarchies. Various embodiments also may provide for memory mapping of data so that memory map changes are reflected in persistent storage while ensuring consistency between memory map changes and writes. For example, by transforming a ZFS file system disk-based storage into ZFS cloud-based storage, the ZFS file system gains the elastic nature of cloud storage.

A system, method and program product for handling potentially problematic events in an enterprise computing platform. A method is disclosed that includes: determining whether a received event includes a feature driven folder processing event; in response to a determination that the event is not a feature driven folder processing event, process the event and determine whether a file count for a folder is impacted; in response to the file count being impacted, evaluate the file count relative to a set of processing thresholds to determine which feature driven folder processing events are enabled for the folder, and to update associated metadata for the folder; and in response to a determination that the event is a feature driven folder processing event, using the computing device to check the metadata for an associated folder to determine whether a requested feature is enabled.

A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: monitoring data center assets within a data center; identifying an issue within the data center, the issue being associated with an operational situation associated with a particular component of the data center; determining whether data associated with the issue corresponds to predefined conditional criteria; and, triggering an autonomous widget operation in response to a determination of the data associated with the issue corresponding to the predefined conditional criteria, the autonomous widget operation executing a particular autonomous widget.

The disclosed embodiments include a method for identifying a performance metric to diagnose a cause of a performance issues of virtual machine. The method includes obtaining data of a virtual machine, an indication that a storage volume contains data of the virtual machine, data about the storage volume, and an identification of the storage volume. The data of the virtual machine is correlated with the data about the storage volume based on the indication that the storage volume contains data of the virtual machine and the identification of the storage volume. A performance metric is identified based at least in part on an outcome of the correlating. The performance metric indicates that the storage volume is a cause of a performance issue of the virtual machine. A state related to the storage volume is changed to mitigate the cause of the performance issue of the virtual machine.