US 12,254,331 B2
Systems and methods of auto-scaling a virtual desktop environment
Vadim Vladimirskiy, Chicago, IL (US)
Assigned to Nerdio, Inc., Chicago, IL (US)
Filed by Nerdio, Inc., Chicago, IL (US)
Filed on Apr. 15, 2024, as Appl. No. 18/635,907.
Application 18/635,907 is a continuation of application No. 17/696,311, filed on Mar. 16, 2022, granted, now 11,960,913.
Claims priority of provisional application 63/161,700, filed on Mar. 16, 2021.
Prior Publication US 2024/0256310 A1, Aug. 1, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 9/451 (2018.01); G06F 9/50 (2006.01)
CPC G06F 9/452 (2018.02) [G06F 9/5077 (2013.01); G06F 2209/5011 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A system for dynamically auto-scaling allocated capacity of a virtual desktop environment, the system comprising: a scalable virtual desktop environment comprising a first set of one or more session host virtual machines providing base capacity resources, including base compute resources and base storage resources, and a second set of one or more session host virtual machines providing burst capacity resources, including burst compute resources and burst storage resources, wherein the base capacity resources are always available to host virtual desktops, wherein the burst capacity resources are temporarily created on demand to host virtual desktops; a server including a controller controlling an operation of the base capacity resources and the burst capacity resources; a memory coupled to the controller, wherein the memory is configured to store program instructions executable by the controller; wherein, in response to executing the program instructions, the controller is configured to: provide a dynamic cost estimation tool including a host pool sizing control and a scaling logic control, wherein the scaling logic control includes a user-selectable auto-scaling trigger and a user-selectable conditional auto-scaling action; in response to inputs to the dynamic cost estimation tool, display a dynamic cost estimation graphical user interface including a maximum estimated monthly cost for compute and a maximum monthly cost for storage; execute, via an auto-scaling module, the user-selectable conditional auto-scaling action in response to recognition of the user-selectable auto-scaling trigger, wherein the user-selectable conditional auto-scaling action comprises powering on or powering off one or more base capacity resources or creating or destroying one or more burst capacity resources.