Abstract: A system for dynamically auto-scaling allocated capacity of a virtual desktop environment includes: base capacity resources and burst capacity resources and memory coupled to a controller; wherein, in response to executing program instructions, the controller is configured to: in response to receiving a log in request from a first user device, connect the first user device to a first host pool to which the first device user is assigned; execute a load-balancing module to determine a first session host virtual machine to which to connect the first user device; and execute an auto-scaling module comprising a user-selectable auto-scaling trigger and a user-selectable conditional auto-scaling action, wherein, in response to recognition of the conditional auto-scaling action, the controller powers on or powers off one or more base capacity resources or creates or destroys one or more burst capacity resources.

Abstract: The present disclosure provides a scalable, standardized IT deployment environment that allows for deployment to any public or private cloud automatically, and that is resizable such that the individual resources can be released (“turned off”) when not needed and powered on when use is expected. Additionally, the present disclosure provides a cost calculation system for better understanding the costs of the IT environment as early as the pre-provisioning stage. The present disclosure also provides a system for proactively testing productivity and efficiency within the IT environment, the results of which can be fed back into the autoscaling mechanism.

Abstract: A system for dynamically auto-scaling allocated capacity of a virtual desktop environment includes: base capacity resources and burst capacity resources and memory coupled to a controller; wherein, in response to executing program instructions, the controller is configured to: in response to receiving a log in request from a first user device, connect the first user device to a first host pool to which the first device user is assigned; execute a load-balancing module to determine a first session host virtual machine to which to connect the first user device; and execute an auto-scaling module comprising a user-selectable auto-scaling trigger and a user-selectable conditional auto-scaling action, wherein, in response to recognition of the conditional auto-scaling action, the controller powers on or powers off one or more base capacity resources or creates or destroys one or more burst capacity resources.

Abstract: The present disclosure provides a scalable, standardized IT deployment environment that allows for deployment to any public or private cloud automatically, and without human interaction, that is resizable such that the individual resources can be released (“turned off”) when not needed and powered on when use is expected. Additionally, the present disclosure provides a cost calculation system for better understanding the costs of the IT environment as early as the pre-provisioning stage. The present disclosure also provides a system for proactively testing productivity and efficiency within the IT environment, the results of which can be fed back into the autoscaling mechanism.

Abstract: A system for optimizing OS disk resource utilization in a dynamically auto-scaling virtual desktop environment includes: a scalable virtual desktop environment in which a VM is selectively powered on and off, wherein the VM is associated with a corresponding OS disk that is alternatively assigned to a higher performance OS disk tier or a lower cost OS disk tier; a server including a controller controlling the powering on and the powering off of the VM and assigning the OS disk tier to the VM; and a memory coupled to the controller, wherein the memory stores executable code programming the controller to: in response to the controller powering off the VM, assign the lower cost OS disk tier to the virtual machine; and prior to the controller powering on the virtual machine, assign the higher performance OS disk tier to the virtual machine.

Abstract: A system including: VMs selectively powered on and off, wherein the VMs store files on a storage volume whose size can be increased and decreased; and a controller that, in response to storage volume usage, adjusts the storage volume to ensure that it is no less than the current storage volume usage plus a minimum free space and no higher than a maximum storage volume size; monitors a latency; when the latency exceeds a high threshold for a given duration, increases the storage volume size until either the latency is below a high threshold or the storage volume size reaches a maximum storage volume size; and when the latency is below a low threshold for a given duration, reduces the storage volume size until either the latency is above the low threshold or the storage volume size is the current storage volume usage plus the minimum free space.

Abstract: A system for dynamically auto-scaling allocated capacity of a virtual desktop environment includes: base capacity resources and burst capacity resources and memory coupled to a controller; wherein, in response to executing program instructions, the controller is configured to: in response to receiving a log in request from a first user device, connect the first user device to a first host pool to which the first device user is assigned; execute a load-balancing module to determine a first session host virtual machine to which to connect the first user device; and execute an auto-scaling module comprising a user-selectable auto-scaling trigger and a user-selectable conditional auto-scaling action, wherein, in response to recognition of the conditional auto-scaling action, the controller powers on or powers off one or more base capacity resources or creates or destroys one or more burst capacity resources.

Abstract: A system for dynamically auto-scaling allocated capacity of a virtual desktop environment includes: base capacity resources and burst capacity resources and memory coupled to a controller; wherein, in response to executing program instructions, the controller is configured to: in response to receiving a log in request from a first user device, connect the first user device to a first host pool to which the first device user is assigned; execute a load-balancing module to determine a first session host virtual machine to which to connect the first user device; and execute an auto-scaling module comprising a user-selectable auto-scaling trigger and a user-selectable conditional auto-scaling action, wherein, in response to recognition of the conditional auto-scaling action, the controller powers on or powers off one or more base capacity resources or creates or destroys one or more burst capacity resources.

Abstract: The present disclosure provides a scalable, standardized IT deployment environment that allows for deployment to any public or private cloud automatically, and without human interaction, that is resizable such that the individual resources can be released (“turned off”) when not needed and powered on when use is expected. Additionally, the present disclosure provides a cost calculation system for better understanding the costs of the IT environment as early as the pre-provisioning stage. The present disclosure also provides a system for proactively testing productivity and efficiency within the IT environment, the results of which can be fed back into the autoscaling mechanism.

Abstract: The present disclosure provides a scalable, standardized IT deployment environment that allows for deployment to any public or private cloud automatically, and without human interaction, that is resizable such that the individual resources can be released (“turned off”) when not needed and powered on when use is expected. Additionally, the present disclosure provides a cost calculation system for better understanding the costs of the IT environment as early as the pre-provisioning stage. The present disclosure also provides a system for proactively testing productivity and efficiency within the IT environment, the results of which can be fed back into the autoscaling mechanism.

Abstract: The present disclosure provides a scalable, standardized IT deployment environment that allows for deployment to any public or private cloud automatically, and without human interaction, that is resizable such that the individual resources can be released (“turned off”) when not needed and powered on when use is expected. Additionally, the present disclosure provides a cost calculation system for better understanding the costs of the IT environment as early as the pre-provisioning stage. The present disclosure also provides a system for proactively testing productivity and efficiency within the IT environment, the results of which can be fed back into the autoscaling mechanism.

Abstract: The present disclosure provides a scalable, standardized IT deployment environment that allows for deployment to any public or private cloud automatically, and without human interaction, that is resizable such that the individual resources can be released (“turned off”) when not needed and powered on when use is expected. Additionally, the present disclosure provides a cost calculation system for better understanding the costs of the IT environment as early as the pre-provisioning stage. The present disclosure also provides a system for proactively testing productivity and efficiency within the IT environment, the results of which can be fed back into the autoscaling mechanism.

Abstract: The present disclosure provides a scalable, standardized IT deployment environment that allows for deployment to any public or private cloud automatically, and without human interaction, that is resizable such that the individual resources can be released (“turned off”) when not needed and powered on when use is expected. Additionally, the present disclosure provides a cost calculation system for better understanding the costs of the IT environment as early as the pre-provisioning stage. The present disclosure also provides a system for proactively testing productivity and efficiency within the IT environment, the results of which can be fed back into the autoscaling mechanism.