An instruction to perform load testing is sent to a mobile device where an application running on the mobile device determines whether the mobile device is in a state where load testing is permitted. In response to receiving the instruction, the application running on the mobile device performs load testing on a web server if the mobile device is in the state where load testing is permitted. Performance information associated with the load testing is received from the application running on the mobile device and the performance information associated with the load testing is displayed.

This application provides a trace chain information query method, including: receiving, by a trace chain server, first trace chain information sent by a first service node and second trace chain information sent by a second service node, where the first service node is a service node in a first trace chain, the second service node is a service node in a second trace chain, both the first trace chain and the second trace chain are generated as triggered by a same user operation, the first trace chain information includes a group identifier, the second trace chain information includes the group identifier, and the group identifier is used to indicate the user operation; and finding, by the trace chain server, the first trace chain information and the second trace chain information based on the group identifier.

A method, computer system, and computer program product for managing application availability in a micro services environment. A monitoring application listens for an event message that indicates an unavailability of critical data. The monitoring application receives the event message over a message pipeline. The monitoring application is critical data agnostic, such that the monitoring application is unaware of the critical data required by the monitored application. Responsive to receiving the event message, the monitoring application interprets the event information within the execution context of the monitored application. The monitoring application identifies a status of the monitored application based on the interpreted event information. The monitoring application updates a status indicator of the monitored application within the execution context, but not within other execution contexts of the monitored application.

Methods and systems for evaluating metrics (e.g., quality of service metrics) corresponding to a monitored computer, detecting metric anomalies, and issuing alerts, are disclosed. A metrics collecting agent, operating on a monitored computer, collects metrics corresponding to the monitored computer and/or one or more monitored services. These metrics are transmitted to a monitoring server that dynamically determines metric thresholds corresponding to normal metrics and anomalous metrics. Using these metric thresholds, along with a machine learning model, the monitoring server can determine whether one or more metrics are anomalous, automatically issue alerts to security and operations teams, and/or transmit a control instruction to the monitored computer in order to fix the issue causing the anomalous metrics.

Systems and methods directed to generating a predicted quality metric are provided. Telemetry data may be received from a from a first group of devices executing first software. A quality metric for the first software may be generated based on the first telemetry data. Telemetry data from a second group of devices may be received, where the second group of devices is different from the first group of devices. Covariates impacting the quality metric based on features included in the first telemetry data and the second telemetry data may be identified, and a coarsened exact matching process may be performed utilizing the identified covariates to generate a predicted quality metric for the first software based on the second group of devices.

An equipment detection system includes a processor, a communication module, and a display module. The processor is configured to detect a connection to an external device. The processor enumerates device information about the external device, obtains user information from a local host, and generates a data structure according to the device information and the user information. The processor is included in the local host. The communication module is configured to transmit the data structure and receive status information. The status information includes a placement space corresponding to the external device or the status of the external device. The status information is associated with the data structure. Moreover, the display module is configured to display the status information.

A resource usage platform is disclosed. The platform performs preemptive container load balancing, auto scaling, and placement in a computing system. Resource usage data is collected from containers and used to train a model that generates inferences regarding resource usage. The resource usage operations are performed based on the inferences and on environment data such as available resources, service needs, and hardware requirements.

A computer device processes frame data provided by running of a computer app, the frame data comprising a plurality of events occurring in the computer ap. A display displays information associated with one or more frames of the plurality of frames. At least one processor of the computer device determines a node graph, in response to input from a user, for one or more events associated with one or more frames from the frame data and that node graph is displayed.

Embodiments for a system and method of monitoring performance metrics of a computer network, by defining key performance indicators for the performance metrics of the computer network, collecting performance data for each of the key performance indicators, and providing one or more anomaly detection policies to define anomalous performance of the computer network using defined threshold values. An anomaly detection policy is applied to the collected performance data to detect abnormal performance and a notification is sent to a user upon each instance of the detected abnormal performance. The anomaly detection policy includes an algorithm applied to the assets, and one or more notification rules that dictate how the notification message is sent to the user.

A system and method providing the user of a media gateway appliance (“MGA”) with graphical depictions of Linux system memory and Android high zone and low zone memory usage, as well as processor usage. The depictions provide a user with a time line of the MGA's utilization of these resources, enabling a user to obtain a detailed history of system resource use and loading. These detailed depictions can be displayed upon a monitor associated with the MGA, or upon a screen associated with a separate device such as a smartphone, tablet or computer system.