VIRTUAL DESKTOP INFRASTRUCTURE OPTIMIZATION
Disclosed are various embodiments for virtual desktop infrastructure optimization. A computing device can create a plurality of predictions for future demand for the VDI, each of the plurality of predictions using a respective one of a plurality of resource models, each representing a separate approach to predict future demand for the VDI. Then, the computing device can calculate a plurality of anticipated resource costs, each of the plurality of anticipated resource costs being based at least in part on a respective one of the plurality of predictions for future demand for the VDI. Moreover, the computing device can include, within a user interface, the plurality of predictions for future demand and the plurality of anticipated resource costs. Then, the computing device can implement a resource model from the plurality of resource models to manage an allocation of resources for the VDI in response to a selection of the resource model through the user interface.
1 . A system, comprising:
a computing device comprising a processor and a memory; and
machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least:
create a plurality of predictions for future demand for virtual desktop infrastructure (VDI), each of the plurality of predictions for future demand being created using a respective one of a plurality of resource models, each of the plurality of resource models representing a separate approach to predict future demand for the VDI;
calculate a plurality of anticipated resource costs, each of the plurality of anticipated resource costs being based at least in part on a respective one of the plurality of predictions for future demand for the VDI;
calculate a respective logon wait risk for each of the plurality of predictions for future demand;
include, within a user interface, the plurality of predictions for future demand for the VDI, the respective logon wait risk for each of the plurality of predictions for future demand, and the plurality of anticipated resource costs for each of the plurality of resource models; and
implement a resource model from the plurality of resource models to manage an allocation of resources for the VDI in response to a selection of the resource model through the user interface.
2 . The system of claim 1 , wherein the machine-readable instructions that cause the computing device to create the plurality of resource models further cause the computing device to at least:
collect historical usage data for the VDI; and
train each of the plurality of resource models based at least in part on the historical usage data for the VDI.
3 . The system of claim 1 , wherein the machine-readable instructions that cause the computing device to implement the resource model to manage the allocation of resources for the VDI further cause the computing device to at least:
periodically determine, based at least in part on the resource model, a number of virtual desktops needed; and
adjust the allocation of resources for the VDI based at least in part on the number of virtual desktops needed.
4 . The system of claim 1 , wherein the machine-readable instructions further cause the computing device to at least include, within a user interface, a current number of virtual desktops assigned to end-users and a current number of virtual desktops available to end-users.
5 . The system of claim 1 , wherein at least one of the plurality of resource models is a limit-optimization model.
6 . The system of claim 1 , wherein at least one of the plurality of resource models is an automatic buffer optimization model.
7 . The system of claim 1 , wherein at least one of the plurality of resource models is a prediction based optimization model.
8 . A method, comprising:
creating a plurality of predictions for future demand for virtual desktop infrastructure (VDI), each of the plurality of predictions for future demand being created using a respective one of a plurality of resource models, each of the plurality of resource models representing a separate approach to predict future demand for the VDI;
calculating a plurality of anticipated resource costs, each of the plurality of anticipated resource costs being based at least in part on a respective one of the plurality of predictions for future demand for the VDI;
calculating a respective logon wait risk for each of the plurality of predictions for future demand;
including, within a user interface, the plurality of predictions for future demand for the VDI, the respective logon wait risk for each of the plurality of predictions for future demand, and the plurality of anticipated resource costs for each of the plurality of resource models; and
implementing a resource model from the plurality of resource models to manage an allocation of resources for the VDI in response to a selection of the resource model through the user interface.
9 . The method of claim 8 , wherein creating the plurality of resource models further comprises:
collecting historical usage data for the VDI; and
training each of the plurality of resource models based at least in part on the historical usage data for the VDI.
10 . The method of claim 8 , wherein implementing the resource model to manage the allocation of resources for the VDI further comprises:
periodically determining, based at least in part on the resource model, a number of virtual desktops needed; and
adjusting the allocation of resources for the VDI based at least in part on the number of virtual desktops needed.
11 . The method of claim 8 , further comprising including, within a user interface, a current number of virtual desktops assigned to end-users and a current number of virtual desktops available to end-users.
12 . The method of claim 8 , wherein at least one of the plurality of resource models is a limit-optimization model.
13 . The method of claim 8 , wherein at least one of the plurality of resource models is an automatic buffer optimization model.
14 . The method of claim 8 , wherein at least one of the plurality of resource models is a prediction based optimization model.
15 . A non-transitory, computer-readable medium, comprising machine-readable instructions that, when executed by a processor of a computing device, cause the computing device to at least:
create a plurality of predictions for future demand for virtual desktop infrastructure (VDI), each of the plurality of predictions for future demand being created using a respective one of a plurality of resource models, each of the plurality of resource models representing a separate approach to predict future demand for the VDI;
calculate a plurality of anticipated resource costs, each of the plurality of anticipated resource costs being based at least in part on a respective one of the plurality of predictions for future demand for the VDI;
calculate a respective logon wait risk for each of the plurality of predictions for future demand;
include, within a user interface, the plurality of predictions for future demand for the VDI, the respective logon wait risk for each of the plurality of predictions for future demand, and the plurality of anticipated resource costs for each of the plurality of resource models; and
implement a resource model from the plurality of resource models to manage an allocation of resources for the VDI in response to a selection of the resource model through the user interface.
16 . The non-transitory, computer-readable medium of claim 15 , wherein the machine-readable instructions that cause the computing device to create the plurality of resource models further cause the computing device to at least:
collect historical usage data for the VDI; and
train each of the plurality of resource models based at least in part on the historical usage data for the VDI.
17 . The non-transitory, computer-readable medium of claim 15 , wherein the machine-readable instructions that cause the computing device to implement the resource model to manage the allocation of resources for the VDI further cause the computing device to at least:
periodically determine, based at least in part on the resource model, a number of virtual desktops needed; and
adjust the allocation of resources for the VDI based at least in part on the number of virtual desktops needed.
18 . The non-transitory, computer-readable medium of claim 15 , wherein at least one of the plurality of resource models is a limit-optimization model.
19 . The non-transitory, computer-readable medium of claim 15 , wherein at least one of the plurality of resource models is an automatic buffer optimization model.
20 . The non-transitory, computer-readable medium of claim 15 , wherein at least one of the plurality of resource models is a prediction based optimization model.