Intelligently managing resource utilization in desktop virtualization environments

Abstract

Resource utilization can be intelligently managed in desktop virtualization environments. Utilization of client and server resources can be tracked during sessions that are established to access desktops. Machine learning models can be applied to the client and server resource utilization to create policies that define configuration settings for dynamically optimizing sessions based on the current utilization of resources during the sessions.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]N / ABACKGROUND[0002]Desktop virtualization refers to various techniques for hosting a desktop on a server while enabling a client to access the desktop over a network using a remote display protocol. FIG. 1 provides a general example of a desktop virtualization environment 100 that includes a number of clients 102-1 through 102-n (referenced generally herein as client(s) 102) in communication with a server 104 via a network 106. Server 104 may be configured to employ various techniques to host multiple desktops simultaneously. Clients 102 can remotely access these desktops using any of several remote display protocols such as the Remote Desktop Protocol (RDP), the Citrix® Independent Computing Architecture (ICA), the PC over IP (PCoIP) Protocol, etc.[0003]Client 102 may represent any computing device capable of implementing a remote display protocol to access a desktop hosted on server 104 including a desktop computer, a laptop, a thin cli...

AI Technical Summary

Benefits of technology

This approach intelligently manages resource utilization, ensuring efficient use of resources on both clients and servers, enhancing user experience and extending battery life by dynamically adjusting settings according to real-time conditions.

Problems solved by technology

In any case, each of the desktops will consume resources of server 104.

If tasks are not offloaded properly, resource utilization on server 104 may become excessive and limit the number of VMs that can be hosted.

On the other hand, when tasks are offloaded to clients 102, resource utilization on clients 102 may become excessive and degrade the user experience.

For example, if multimedia redirection is performed when client 102 is running on battery, the rendering of the multimedia content may quickly drain the battery.

Alternatively, while running on battery, client 102 may employ a power saver mode which may result in a jittery display of the multimedia content or other display data.

Furthermore, with the various types and capabilities of clients 102, what may be an appropriate level of offloading for one client may be excessive for another.

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