Abstract:
One or more processing devices access a service definition for a service provided by one or more entities that each produce machine data or about which machine data is generated. The service definition identifies the entities that provide the service and, for each entity, identifying information for locating machine data pertaining to that entity. The processing devices access a key performance indicator (KPI) for the service that is defined by a search query that produces a value derived from the machine data pertaining to the entities identified in the service definition. The value indicates how the service is performing at a point in time or during a period of time and indicates a state of the KPI. A graphical interface is displayed and an indication of at least one threshold, which defines an end of a range of values representing a state of the KPI, for the KPI is received.
Abstract:
Network connected devices are controlled via the transmission of action messages to prevent or correct conditions that impair the operation of the networked information technology (IT) assets. The service monitoring system (SMS) monitoring the IT environment groups together related notable events that are received during system operation. Automatic processes dynamically determine grouping operations that automatically correlate the events without requiring, for example, a set of declarative grouping rules. Event grouping may be performed on a by-service basis to facilitate the complex processing of predicting undesirable system conditions that may be prevented or reduced by transmission of the action messages to the appropriate assets. Event grouping operations may be directed with control information maintained via user interface.
Abstract:
An automatic service monitor in an information technology environment has its operation controlled by information that, in part, defines entities that perform services and defines key performance indicators (KPIs) that indicate measures of performance of the services. Additional information controls the operation of the service monitor with respect to identifying and adapting for KPIs based on the non-normal data caused by maintenance work or other causes. Such adaptation may include changes in how reported information appears to the user.
Abstract:
The disclosed system and method acquire and store performance measurements relating to performance of a component in an information technology (IT) environment and log data produced by the IT environment, in association with corresponding time stamps. The disclosed system and method correlate at least one of the performance measurements with at least one of the portions of log data.
Abstract:
One or more processing devices access a service definition for a service provided by one or more entities that each produce machine data or about which machine data is generated. The service definition identifies the entities that provide the service and, for each entity, definitional information includes information for identifying machine data pertaining to that entity. The processing devices access a key performance indicator (KPI) for the service that is defined by a search query that produces a value derived from the machine data pertaining to the entities identified in the service definition. The value indicates how the service is performing at a point in time or during a period of time and indicates a state of the KPI. A graphical interface is displayed and an indication of at least one threshold, which defines an end of a range of values representing a state of the KPI, for the KPI is received.
Abstract:
The disclosed system and method acquire and store performance measurements relating to performance of a component in an information technology (IT) environment and log data produced by the IT environment, in association with corresponding time stamps. The disclosed system and method correlate at least one of the performance measurements with at least one of the portions of log data.
Abstract:
A system, method and graphical user interface (GUI) for creating a new correlation search based on fluctuations in key performance indicators (KPIs) displayed in a set of graph lanes. The graph lanes may provide graphical visualizations of the KPIs associated with one or more services and may assist a user in identifying a situation (e.g., problem or a pattern of interest) in the performance of the services. The graph lanes can be adjusted (e.g., add graph lanes, zooming-in) in order to display the situation, at which point a new correlation search may be generated to detect if the situation reoccurs. The system may generate the new correlation search by iterating through the set of graph lanes and analyzing the fluctuations of each KPI to determine triggering criteria. The system may then run the correlation search and generate a notable event or alarm when the situation reoccurs.
Abstract:
The disclosed system and method acquire and store performance measurements relating to performance of a component in an information technology (IT) environment and log data produced by the IT environment, in association with corresponding time stamps. The disclosed system and method correlate at least one of the performance measurements with at least one of the portions of log data.
Abstract:
The disclosed system and method acquire and store performance measurements relating to performance of a component in an information technology (IT) environment and log data produced by the IT environment, in association with corresponding time stamps. The disclosed system and method correlate at least one of the performance measurements with at least one of the portions of log data.
Abstract:
Techniques are disclosed for providing adaptive thresholding technology for Key Performance Indicators (KPIs). Adaptive thresholding technology may automatically assign new values or adjust existing values for one or more thresholds of one or more time policies. Assigning threshold values using adaptive thresholding may involve identifying training data (e.g., historical data, simulated data, or example data) for the time frames and analyzing the training data to identify variations within the data (e.g., patterns, distributions, trends). A threshold value may be determined based on the variations and may be assigned to one or more of the thresholds without additional user intervention.