Abstract: An example method of enabling a virtual infrastructure management (VIM) appliance for lifecycle management includes: identifying, by a cloud platform executing in a public cloud, a manager VIM appliance for the VIM appliance, the manager VIM appliance and the VIM appliance executing in at least one on-premises data center of an on-premises environment; obtaining information related to a management cluster having the manager VIM appliance and a virtual machine (VM) executing the VIM appliance; creating and applying, by the cloud platform in response to the information, a desired state for both the manager VIM appliance and the VIM appliance; and updating the cloud platform with a topology of the manager VIM appliance and the VIM appliance in the management cluster.

Abstract: Certain embodiments described herein are generally directed to techniques for determining items of inventory of a data center to which a user has access. Embodiments include receiving permission information indicating specific user permissions assigned to particular items of a plurality of items in an inventory of data center resources, wherein items of the plurality of items are organized in a hierarchical manner across nodes of a hierarchical tree. Embodiments include assigning categories to the plurality of items based on the permission information, wherein each of the particular items is assigned a unique category based on the specific user permissions and each of the plurality of items that is not in the particular items and that has a parent node in the hierarchical tree is assigned a category corresponding to the parent node. Embodiments include storing category information in a data store based on the assigning of the categories.

Abstract: Drift is automatically detected in configuration of services running in a management appliance of a software-defined data center. A method of automatically detecting drift includes: in response to a notification of a change in a configuration of a first service enabled for proactive drift detection, transmitting a first request to compute drift in the configuration of the first service to a plug-in of the first service, the first request including the change in the configuration of the first service; periodically, at designated time intervals, transmitting a second request to compute drift in the configuration of a second service enabled for passive drift detection, to the plug-in of the second service, the second request including a current state of the configuration of the second service; and notifying a desired state management service of the computed drift in the configuration of the first and second services.

Abstract: Scalar values are assigned to hierarchically-arranged items of inventory of one or more data centers, which may be physical or software-defined, based on the positions of the inventory items in the hierarchy. The scalar values are assigned to the items such that they have increasing values when the items are traversed during a depth-first search of the hierarchy. With this assignment, items of inventory to which a user has access permissions, or any other type of permissions, can be specified as one or more ranges of the scalar values, and such items can be determined from the specified one or more ranges.

Abstract: A method of managing configurations of a software-defined data center (SDDC) includes: retrieving a current configuration of a first management appliance of the SDDC and a current configuration of a second management appliance of the SDDC; calling a first custom resource object of a container orchestration platform to acquire a desired configuration of the first management appliance and calling a second custom resource object of the container orchestration platform to acquire a desired configuration of the second management appliance; determining a difference between the current and desired configurations of the first management appliance and instructing the first management appliance to apply the desired configuration of the first management appliance; and determining a difference between the current and desired configurations of the second management appliance and instructing the second management appliance to apply the desired configuration of the second management appliance.

Abstract: Items of inventory of a data center are organized in a hierarchical manner across nodes of at least one hierarchical tree. A method of generating items of inventory of the data center to which a user has access includes generating a plurality of first user access paths based on permissions given to the user at one or more nodes of a first hierarchical tree, and performing a database look-up on an inventory database using the first user access paths to determine the inventory items of the data center to which the user has access. The inventory database stores for each inventory item of the data center identifying information that uniquely identifies the inventory item and node information indicating the node of the first hierarchical tree where the inventory item is arranged.

Abstract: Described herein are systems, methods, and software to manage the migration of workloads from a first computing system to a second computing system. In one implementation, the first computing system identifies a request to migrate one or more workloads to a second computing system. In response to the request, the first computing system disables one or more services and disables all but one network interface on the first computing system. The first computing system then communicates configuration information to the second computing system and monitors for a cancel notification from the second computing system using the remining network interface. After receiving the cancel notification, the first computing system enables the other network interfaces may initiate the one or more services.

Abstract: Various examples are disclosed for predictive allocation of computing resources in a virtual desktop infrastructure environment. A computing environment can generate a predictive usage model as a time series that forecasts a usage of virtual machines that provide virtual desktop sessions in a virtual desktop infrastructure environment. The computing environment can determine a number of the plurality of virtual machines that will be operating at a future time utilizing the predictive usage model and identify computing resources required for the number of the virtual machines to operate at the future time. The computing environment can allocate the computing resources such that the computing resources are available at the future time.

Abstract: Various examples are disclosed for predictive allocation of computing resources in a virtual desktop infrastructure environment. A computing environment can generate a predictive usage model as a time series that forecasts a usage of virtual machines that provide virtual desktop sessions in a virtual desktop infrastructure environment. The computing environment can determine a number of the plurality of virtual machines that will be operating at a future time utilizing the predictive usage model and identify computing resources required for the number of the virtual machines to operate at the future time. The computing environment can allocate the computing resources such that the computing resources are available at the future time.