In an approach to virtualizing communication channels between one or more hardware components and a controller, a system includes: a first controller implemented in a reconfigurable hardware device; and a virtual platform stratus having a plurality of input/output (I/O) ports for electrically coupling with the one or more hardware components and receiving one or more electrical signals therefrom, and where the VPS is configured to generate one or more data frames from the one or more electrical signals; and where the virtual platform stratus is configured to send the data frames to the first controller and/or provide electrical signaling to the one or the one or more hardware components based on data frames received from the first controller.

Aspects of the subject disclosure may include, for example, identifying a request to install a guest virtual machine on a physical host; identifying a UUID of the physical host; generating a virtual machine reference value; defining a modified UUID of the guest virtual machine comprising the UUID of the physical host and the virtual machine reference value; and assigning the modified UUID to the guest virtual machine, the physical host being identifiable via the modified UUID of the guest virtual machine. Other embodiments are disclosed.

Embodiments described herein provide for an election procedure, in a high availability (“HA”) environment, for a backup controller to assume operations performed by a master controller in the event that the master controller becomes unreachable. The master controller may be associated with (e.g., provisioned on) the same set of hardware as one or more worker nodes, and may control operation of the one or more worker nodes. The election procedure may be performed based on performance metrics, location, or efficiency metrics associated with candidate backup controllers (e.g., cloud-based backup controllers), including performance of communications between particular backup controllers and the one or more worker nodes.

Various techniques for emulating edge locations in cloud-based networks are described. An example method includes generating an emulated edge location in a region. The emulated edge location can include one or more first computing resources in the region. A host in the region may launch a virtualized resource a portion of the one or more first computing resources. Output data that was output by the virtualized resource in response to input data can be received and reported to a user device, which may provide a request to migrate the virtualized resource to a non-emulated edge location. The non-emulated edge location may include one or more second computing resources that are connected to the region by an intermediary network. The virtualized resource can be migrated from the first computing resources to at least one second computing resource in the non-emulated edge location.

Cloud migration may be performed by identifying applications that are currently operating in the enterprise and performing certain determinations as to whether those applications are proper candidates for the migration to the cloud. One example method of operation may provide identifying at least one application operating on an enterprise network, retrieving current usage data of the at least one application, comparing the current usage data of the at least one application to a threshold amount of usage data to determine whether the application has exceeded the threshold amount of usage data. Next, the creation of an instance process may be performed on an entity operating outside the enterprise network and the application may be operated via the instance process and otherwise terminated in the enterprise network to alleviate resources.

A method for containerized workload scheduling can include determining a network state for a first hypervisor in a virtual computing cluster (VCC). The method can further include determining a network state for a second hypervisor. Containerized workload scheduling can further include deploying a container to run a containerized workload on a virtual computing instance (VCI) deployed on the first hypervisor or the second hypervisor based, at least in part, on the determined network state for the first hypervisor and the second hypervisor.

Disclosed are aspects of virtual graphics processing unit (vGPU) scheduling-aware virtual machine migration. Graphics processing units (GPUs) that are compatible with a current virtual GPU (vGPU) profile for a virtual machine are identified. A scheduling policy matching order for a migration of the virtual machine is determined based on a current vGPU scheduling policy for the virtual machine. A destination GPU is selected based on a vGPU scheduling policy of the destination GPU being identified as a best available vGPU scheduling policy according to the scheduling policy matching order. The virtual machine is migrated to the destination GPU.

Aspects of the present disclosure involve systems, methods, devices, and the like for creating an application lifecycle management platform for big data applications. In one embodiment the lifecycle management platform can include a multiple-layer container file that integrates multiple big-data tools/platforms. The system may create a generic template application, create a build environment for the generic template application, create a test environment for the generic template application, and run the built generic template application in the test environment prior to the user writing any new code in the generic template application. In one embodiment, the test environment includes a container management system or virtual machine that launches the big data application (which may be the generic template application before a developer edits the file) on a separate big-data server cluster.

Techniques for optimizing CPU usage in a host system based on VM guest OS power and performance management are provided. In one embodiment, a hypervisor of the host system can capture information from a VM guest OS that pertains to a target power or performance state set by the guest OS for a vCPU of the VM. The hypervisor can then perform, based on the captured information, one or more actions that align usage of host CPU resources by the vCPU with the target power or performance state.

Methods and systems for assessing internet exposure of a cloud-based workload are disclosed. A method comprises accessing at least one cloud provider API to determine a plurality of entities capable of routing traffic in a virtual cloud environment associated with a target account containing the workload, querying the at least one cloud provider API to determine at least one networking configuration of the entities, building a graph connecting the plurality of entities based on the networking configuration, accessing a data structure identifying services publicly accessible via the Internet and capable of serving as an internet proxy; integrating the identified services into the graph; traversing the graph to identify at least one source originating via the Internet and reaching the workload, and outputting a risk notification associated with the workload. Systems and computer-readable media implementing the above method are also disclosed.