Abstract: A system and method intelligently collect performance data from managed electronic devices. A machine learning model (e.g. linear time series forecasting) is used to predict a future workload for each of a selection of devices, and a regression analysis is used to predict how long is likely to be required to collect performance state from each component of each device. These data are then mapped together to predict future overall idle periods of each device, together with components whose performance data may be collected during those periods. The components are grouped in batches according to a relevance order that itself may be determined by applying a machine learning model such as k-nearest neighbors. Then, performance data are collected according to the batches. In this way, performance data may be collected in chunks while avoiding a negative impact on execution of the primary functions of the managed devices.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving, by a systems management console, a network address of a device in a virtual environment, and determining a network address associated with a virtual environment management console based on the received network address of the device in the virtual environment. The method also includes sending, by the systems management console via a systems management agent to the virtual environment management console using the determined network address associated with the virtual environment management console, a request for network addresses of virtual machine (VM) host servers and VMs in the virtual environment. The method also includes receiving, by the systems management console via the systems management agent from the virtual environment management console, the network addresses of the VM host servers and the VMs in the virtual environment and providing a notification of the discovered VM host servers and VMs.

Abstract: In some examples, after a client system encounters a problem, a technical support specialist may connect to the client system via a console. The console may display a graphical representation of a client system that includes a plurality of components. The console may execute a machine learning module to determine one or more potential solutions to the particular problem. Each solution of the one or more solutions may correspond to a previously resolved problem that is similar to the particular problem and may have an associated confidence level determined based on: a similarity of the particular problem to the previously resolved problem, a similarity of the plurality of components to a second plurality of components included in a second client system associated with the previously resolved problem, a similarity of a network topology of the client system to a second network topology of the second client system.

Abstract: In some examples, after a client system encounters a problem, a technical support specialist may connect to the client system via a console. The console may display a graphical representation of a client system that includes a plurality of components. The console may execute a machine learning module to determine one or more potential solutions to the particular problem. Each solution of the one or more solutions may correspond to a previously resolved problem that is similar to the particular problem and may have an associated confidence level determined based on: a similarity of the particular problem to the previously resolved problem, a similarity of the plurality of components to a second plurality of components included in a second client system associated with the previously resolved problem, a similarity of a network topology of the client system to a second network topology of the second client system.