Systems and methods for monitoring inter-application communications in complex computing ecosystems

    公开(公告)号:US11848834B2

    公开(公告)日:2023-12-19

    申请号:US17867118

    申请日:2022-07-18

    CPC classification number: H04L43/04 G16H20/10 H04L43/08 G06F9/54

    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. As such, the monitoring system provides consolidated visibility to the ecosystem.

    SYSTEMS AND METHODS FOR MONITORING INTER-APPLICATION COMMUNICATIONS IN COMPLEX COMPUTING ECOSYSTEMS

    公开(公告)号:US20220353160A1

    公开(公告)日:2022-11-03

    申请号:US17867118

    申请日:2022-07-18

    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. As such, the monitoring system provides consolidated visibility to the ecosystem.

    Machine learning models for automated anomaly detection for application infrastructure components

    公开(公告)号:US11416369B1

    公开(公告)日:2022-08-16

    申请号:US17126250

    申请日:2020-12-18

    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. The instructions include obtaining measured performance metrics of an application infrastructure component, processing the measured performance metrics with the first machine learning model to produce the component health status output for the component, processing the measured performance metrics with the second machine learning model to produce the component health score output for the first component, and generating an output to transform a display visible to an operator.

    SYSTEMS AND METHODS FOR MONITORING INTER-APPLICATION COMMUNICATIONS IN COMPLEX COMPUTING ECOSYSTEMS

    公开(公告)号:US20240113949A1

    公开(公告)日:2024-04-04

    申请号:US18534914

    申请日:2023-12-11

    CPC classification number: H04L43/04 G16H20/10 H04L43/08 G06F9/54

    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. As such, the monitoring system provides consolidated visibility to the ecosystem.

    MACHINE LEARNING MODELS FOR AUTOMATED ANOMALY DETECTION FOR APPLICATION INFRASTRUCTURE COMPONENTS

    公开(公告)号:US20220391300A1

    公开(公告)日:2022-12-08

    申请号:US17888104

    申请日:2022-08-15

    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. The instructions include obtaining measured performance metrics of an application infrastructure component, processing the measured performance metrics with the first machine learning model to produce the component health status output for the component, processing the measured performance metrics with the second machine learning model to produce the component health score output for the first component, and generating an output to transform a display visible to an operator.

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