Abstract: An architecture for implementing a mechanism for displaying GPU resource usage and dynamically allocating GPU resources in a networked virtualization system is provided. The mechanism compares an initial allocation of GPU resources to virtual machines supported by one or more nodes of the networked virtualization system to a current GPU resource usage by the virtual machines. Based at least in part on the comparison and workloads processed by the virtual machines, the mechanism may reallocate GPU resources to one or more of the virtual machines. A virtual machine is reassigned to a different vGPU profile if reassignment is likely to achieve a more efficient allocation of GPU resources to the virtual machine. A user interface indicating GPU resource usage relative to GPU resource allocation may also be generated and displayed.

Abstract: The present embodiments relate generally to master image management for a virtualization environment such as a virtual desktop infrastructure. Some embodiments are directed toward using image snapshot technology to identify differences between master images. In these and other embodiments, a methodology includes indexing the contents of snapshots of the same master image taken at different points in time to determine the delta between the snapshots, and thus the states of the master image at the different points in time.

Abstract: An architecture for implementing a mechanism for automatically displaying metrics specific to a type of workload being processed by a computer system is provided. The mechanism predicts a classification of the workload based on attributes that characterize the workload using a set of workload profiles and/or a set of classification rules that correlate different combinations of attributes of workloads with different classifications of workloads. Based on the predicted classification of the workload, one or more templates including one or more metrics specific to the classification of workload are identified. The template(s) including the metric(s) specific to the classification of the workload may be identified using set of rules that associate the metric(s) with the predicted classification of the workload. A user interface including the metric(s) is generated based on the template(s). The user interface may then be displayed to a user of the computer system.

Abstract: The present embodiments relate generally to master image management for a virtualization environment such as a virtual desktop infrastructure. Some embodiments are directed toward using image snapshot technology to identify differences between master images. In these and other embodiments, a methodology includes indexing the contents of snapshots of the same master image taken at different points in time to determine the delta between the snapshots, and thus the states of the master image at the different points in time.

Abstract: An architecture for implementing a mechanism for automatically displaying metrics specific to a type of workload being processed by a computer system is provided. The mechanism predicts a classification of the workload based on attributes that characterize the workload using a set of workload profiles and/or a set of classification rules that correlate different combinations of attributes of workloads with different classifications of workloads. Based on the predicted classification of the workload, one or more templates including one or more metrics specific to the classification of workload are identified. The template(s) including the metric(s) specific to the classification of the workload may be identified using set of rules that associate the metric(s) with the predicted classification of the workload. A user interface including the metric(s) is generated based on the template(s). The user interface may then be displayed to a user of the computer system.

Abstract: This disclosure provides systems and methods for determining a duration of a logon event. A start time of a logon event for a virtual machine can be identified. An end time of the logon event for the virtual machine can be identified. A duration of the logon event can be determined, based on a difference between the start time of the logon event and the end time of the logon event. A record including information corresponding to the duration of the logon event, an identification of the virtual machine, and an identification of a username associated with a user who initiated the logon event can be stored in a database. A visual representation of the stored record can be displayed via a graphical user interface.

Abstract: An architecture for implementing a mechanism for displaying GPU resource usage and dynamically allocating GPU resources in a networked virtualization system is provided. The mechanism compares an initial allocation of GPU resources to virtual machines supported by one or more nodes of the networked virtualization system to a current GPU resource usage by the virtual machines. Based at least in part on the comparison and workloads processed by the virtual machines, the mechanism may reallocate GPU resources to one or more of the virtual machines. A virtual machine is reassigned to a different vGPU profile if reassignment is likely to achieve a more efficient allocation of GPU resources to the virtual machine. A user interface indicating GPU resource usage relative to GPU resource allocation may also be generated and displayed.

Abstract: An architecture for implementing a mechanism for displaying GPU resource usage and dynamically allocating GPU resources in a networked virtualization system is provided. The mechanism compares an initial allocation of GPU resources to virtual machines supported by one or more nodes of the networked virtualization system to a current GPU resource usage by the virtual machines. Based at least in part on the comparison and workloads processed by the virtual machines, the mechanism may reallocate GPU resources to one or more of the virtual machines. A virtual machine is reassigned to a different vGPU profile if reassignment is likely to achieve a more efficient allocation of GPU resources to the virtual machine. A user interface indicating GPU resource usage relative to GPU resource allocation may also be generated and displayed.