Abstract: Described herein are systems and methods for determining an upgrade to an application as a cause of a failure in a session. A device may detect a failure in a session via which a client is accessing a first version of an application. The device may identify, in response to detecting the failure, first performance metrics for the first version of the application and second performance metrics for a second version of the application. The device may determine, based at least on the failure, the first performance metrics, and the second performance metrics, that an upgrade to the application to the first version from the second version is a cause of the failure in the session. The device may store an association between the first version of the application and the determination that the upgrade is the cause of the failure in the session.

Abstract: Effectively allocating computing resources to end-users is provided. A system can identify mapping groups comprising a first mapping group that maps first users to corresponding first machines having a first hardware configuration, and a second mapping group that maps second users to corresponding second machines having a second hardware configuration. The system can determine a first metric indicating computing resource utilization of a first machine of the first machines when executing one or more sessions of a first user in the first mapping group, and a second metric indicating computing capacity of the second machines. The system can re-map the first user from the first mapping group to the second mapping group to cause sessions of the first user to execute on a second machine of the second machines.

Abstract: Described embodiments provide systems and methods for classifying a machine by performance. A device may identify, for a first time window, a first plurality of attributes of a machine and a session provided by the machine. The device may determine a first score based at least on a weight applied to each of the first plurality of attributes. The weight may be updated using a second plurality of attributes of the machine and the session provided by the machine for a second time window. The device may determine a probability of failure for the session by applying the first plurality of attributes to a model. The device may generate a second score indicating a performance of the machine as a function of the first score and the probability of failure. The device may classify the machine into a performance level in accordance with the second score.

Abstract: Described embodiments provide systems and methods for analyzing process and resource metrics across client devices. A resource manager may receive metrics on resource utilization of a plurality of processes executing on at least some of a plurality of client devices. The resource manager may select a subset of the plurality of processes based on the metrics. The resource manager may determine a subset of the plurality of client devices which are executing the selected subset of processes. The resource manager may generate an alert which identifies the subset of processes and the subset of client devices which are executing the subset of processes.

Abstract: Effectively allocating computing resources to end-users is provided. A system can identify mapping groups comprising a first mapping group that maps first users to corresponding first machines having a first hardware configuration, and a second mapping group that maps second users to corresponding second machines having a second hardware configuration. The system can determine a first metric indicating computing resource utilization of a first machine of the first machines when executing one or more sessions of a first user in the first mapping group, and a second metric indicating computing capacity of the second machines. The system can re-map the first user from the first mapping group to the second mapping group to cause sessions of the first user to execute on a second machine of the second machines.

Abstract: Described embodiments provide systems and methods for determining bounce-able machines. One or more processors can be coupled to memory. The one or more processors can identify data associated with a history of actions performed on a plurality of machines. The one or more processors can determine, using the data, a change in performance of the plurality of machines if a sequence of actions were applied to one or more of the plurality of machines. The one or more processors can select a machine of the plurality of machines based on at least on the change in performance of the machine satisfying a threshold. The one or more processors can initiate, responsive to the selection, the sequence of actions on the machine.

Abstract: Systems and methods for proactively alerting administrators of upcoming or possible network outages include a server which receives metrics for usage of one or more networks for each workspace application of plurality of workspace applications of a plurality of endpoints across a plurality of different locations of an enterprise. The server may determine a network download speed for each location of the plurality of different locations according to the metrics for each workspace application. The server may generate an alert to be provided to a device of a user associated with a first location of the plurality of different locations responsive to the network download speed for a network of the one or more networks falling below a threshold.

Abstract: Described embodiments provide systems and methods for detection of the degradation of a virtual desktop environment. A computing device may receive data from a plurality of client devices. The computing device may identify a subset of client devices from the plurality of client devices with at least one characteristic in common based on the received data. The computing device may determine a ratio of the identified subset of client devices, the ratio being a comparison of client devices of the subset with a value above a first threshold to a total number of client devices of the subset, and the value being indicative of a characteristic of performance for that client device. The computing device may identify a cause of an anomaly in the performance of the application based on the ratio exceeding a second threshold.

Abstract: Described embodiments provide systems and methods for detection of the degradation of a virtual desktop environment. A computing device may receive data from a plurality of client devices. The computing device may identify a subset of client devices from the plurality of client devices with at least one characteristic in common based on the received data. The computing device may determine a ratio of the identified subset of client devices, the ratio being a comparison of client devices of the subset with a value above a first threshold to a total number of client devices of the subset, and the value being indicative of a characteristic of performance for that client device. The computing device may identify a cause of an anomaly in the performance of the application based on the ratio exceeding a second threshold.

Abstract: Described embodiments provide systems and method for determining action insights to address, correct or fix application failures. A device can identify an assignment of a user to a virtual machine and the assignment can enable launch of an application with use of the same virtual machine on behalf of the user. The virtual machine can be one of a delivery group of virtual machines. A threshold can be determined for the virtual machine that indicates a likely failure of the virtual machine to launch the application based data about performance of the delivery group. The device can modify the assignment of a user from the virtual machine to another virtual machine of the delivery group based on a comparison of a load on the virtual machine and the determined threshold, so as to avoid failure of the application to launch.

Abstract: Described embodiments provide systems and methods for detection of the degradation of a virtual desktop environment. A computing device may receive data from a plurality of client devices. The computing device may identify a subset of client devices from the plurality of client devices with at least one characteristic in common based on the received data. The computing device may determine a ratio of the identified subset of client devices, the ratio being a comparison of client devices of the subset with a value above a first threshold to a total number of client devices of the subset, and the value being indicative of a characteristic of performance for that client device. The computing device may identify a cause of an anomaly in the performance of the application based on the ratio exceeding a second threshold.