Abstract: A monitoring system for mapping and monitoring inter-application communications in a computing ecosystem is described. The monitoring system provides consolidated visibility to computing ecosystems by providing end-to-end mapping and monitoring of inter-application communications and events, changes, incidents, and status information of applications, services, and systems. As described, the monitoring system is configured to (a) identify communication paths linking the host devices, (b) generate an ecosystem map based on the communication paths, (c) transmit a monitoring signal to the network, (d) receive a monitoring response from the host devices in response to the monitoring signal including at least a first status, (e) process the monitoring response with the ecosystem map to generate an active ecosystem map, and (f) display the active ecosystem map including the host devices and at least one status associated with the host devices.

Abstract: A system for automated detection of anomalous performance of application infrastructure components includes memory hardware and processor hardware communicatively coupled together. The processor hardware is configured to train the at least one machine learning model using measured historical performance metrics of a first component of the application infrastructure components. The processor hardware is configured to use the at least one machine learning model to generate key performance indicators for the first component, based on a reporting window time period associated with measured performance metrics. The processor hardware is configured to, in response to the key performance indicators indicating a performance anomaly condition for the first component, automatically perform a self-healing operation associated with the key performance indicators.

Abstract: A system for automated detection of anomalous performance of application infrastructure components includes memory hardware and processor hardware communicatively coupled together. The processor hardware is configured to train the at least one machine learning model using measured historical performance metrics of a first component of the application infrastructure components. The processor hardware is configured to use the at least one machine learning model to generate key performance indicators for the first component, based on a reporting window time period associated with measured performance metrics. The processor hardware is configured to, in response to the key performance indicators indicating a performance anomaly condition for the first component, automatically perform a self-healing operation associated with the key performance indicators.

Abstract: A monitoring system for mapping and monitoring inter-application communications in a computing ecosystem is described. The monitoring system provides consolidated visibility to computing ecosystems by providing end-to-end mapping and monitoring of inter-application communications and events, changes, incidents, and status information of applications, services, and systems. As described, the monitoring system is configured to (a) identify communication paths linking the host devices, (b) generate an ecosystem map based on the communication paths, (c) transmit a monitoring signal to the network, (d) receive a monitoring response from the host devices in response to the monitoring signal including at least a first status, (e) process the monitoring response with the ecosystem map to generate an active ecosystem map, and (f) display the active ecosystem map including the host devices and at least one status associated with the host devices.

Abstract: A computer system includes processor hardware configured to execute instructions from memory hardware. The instructions include training first and second machine learning models with the measured historical performance metrics to generate a component health status output and a component health score output, respectively, and generating a plurality of elements for display in a multi-level application monitoring interface. The measured historical performance metrics include at least one of a component response time, a component volume, a component memory utilization, and a component processor utilization.

Abstract: A monitoring system for mapping and monitoring inter-application communications in a computing ecosystem is described. The monitoring system provides consolidated visibility to computing ecosystems by providing end-to-end mapping and monitoring of inter-application communications and events, changes, incidents, and status information of applications, services, and systems. As described, the monitoring system is configured to (a) identify communication paths linking the host devices, (b) generate an ecosystem map based on the communication paths, (c) transmit a monitoring signal to the network, (d) receive a monitoring response from the host devices in response to the monitoring signal including at least a first status, (e) process the monitoring response with the ecosystem map to generate an active ecosystem map, and (f) display the active ecosystem map including the host devices and at least one status associated with the host devices.

Abstract: A method is provided for diagnosing probable root causes of anomalies in computing environments. The method is performed by an anomaly analytics server in networked communication with hosts. The method includes (a) receiving a topology map; (b) receiving event data associated with each host; (c) receiving an anomaly baseline defining thresholds for identifying an anomalous status in each host application; (d) analyzing the event data and the anomaly baseline to determine a host anomaly status for each host; (e) analyzing the host anomaly status and the topology map to determine a tier anomaly status for each of the host tiers; (f) identifying at least one host tier as a root cause host tier by processing the tier anomaly statuses and the set of tier dependencies; and (g) generating an alert identifying each root cause host tier as a probable root cause of an anomaly.

Abstract: A computer system includes processor hardware configured to execute instructions from memory hardware. The instructions include training first and second machine learning models with the measured historical performance metrics to generate a component health status output and a component health score output, respectively, and generating a plurality of elements for display in a multi-level application monitoring interface. The measured historical performance metrics include at least one of a component response time, a component volume, a component memory utilization, and a component processor utilization.

Abstract: A monitoring system for mapping and monitoring inter-application communications in a computing ecosystem is described. The monitoring system provides consolidated visibility to computing ecosystems by providing end-to-end mapping and monitoring of inter-application communications and events, changes, incidents, and status information of applications, services, and systems. As described, the monitoring system is configured to (a) identify communication paths linking the host devices, (b) generate an ecosystem map based on the communication paths, (c) transmit a monitoring signal to the network, (d) receive a monitoring response from the host devices in response to the monitoring signal including at least a first status, (e) process the monitoring response with the ecosystem map to generate an active ecosystem map, and (f) display the active ecosystem map including the host devices and at least one status associated with the host devices.

Abstract: A computer system includes processor hardware configured to execute instructions from memory hardware. The instructions include training first and second machine learning models with the measured historical performance metrics to generate a component health status output and a component health score output, respectively, and generating a plurality of elements for display in a multi-level application monitoring interface. The measured historical performance metrics include at least one of a component response time, a component volume, a component memory utilization, and a component processor utilization.

Abstract: A monitoring system for mapping and monitoring inter-application communications in a computing ecosystem is described. The monitoring system provides consolidated visibility to computing ecosystems by providing end-to-end mapping and monitoring of inter-application communications and events, changes, incidents, and status information of applications, services, and systems. As described, the monitoring system is configured to (a) identify communication paths linking the host devices, (b) generate an ecosystem map based on the communication paths, (c) transmit a monitoring signal to the network, (d) receive a monitoring response from the host devices in response to the monitoring signal including at least a first status, (e) process the monitoring response with the ecosystem map to generate an active ecosystem map, and (f) display the active ecosystem map including the host devices and at least one status associated with the host devices.

Abstract: A method is provided for diagnosing probable root causes of anomalies in computing environments. The method is performed by an anomaly analytics server in networked communication with hosts. The method includes (a) receiving a topology map; (b) receiving event data associated with each host; (c) receiving an anomaly baseline defining thresholds for identifying an anomalous status in each host application; (d) analyzing the event data and the anomaly baseline to determine a host anomaly status for each host; (e) analyzing the host anomaly status and the topology map to determine a tier anomaly status for each of the host tiers; (f) identifying at least one host tier as a root cause host tier by processing the tier anomaly statuses and the set of tier dependencies; and (g) generating an alert identifying each root cause host tier as a probable root cause of an anomaly.

Abstract: A method is provided for diagnosing probable root causes of anomalies in computing environments. The method is performed by an anomaly analytics server in networked communication with hosts. The method includes (a) receiving a topology map; (b) receiving event data associated with each host; (c) receiving an anomaly baseline defining thresholds for identifying an anomalous status in each host application; (d) analyzing the event data and the anomaly baseline to determine a host anomaly status for each host; (e) analyzing the host anomaly status and the topology map to determine a tier anomaly status for each of the host tiers; (f) identifying at least one host tier as a root cause host tier by processing the tier anomaly statuses and the set of tier dependencies; and (g) generating an alert identifying each root cause host tier as a probable root cause of an anomaly.