An example method of establishing trust between a cross-cluster control plane (xCCP) and a cluster control plane (CCP) of an autonomous cluster of hosts in a virtualized computing system includes: providing, by the xCCP, trust data of the xCCP to a hypervisor of a host in the autonomous cluster that is executing the CCP; providing, by the hypervisor, the trust data to the CCP through a volume attached to a virtual machine (VM) that executes the CCP; persisting, by the CCP, the trust data in a database; and accessing, by a security token service (STS) of the CCP, the trust data in the database to authenticate access to the CCP by the xCCP.

A method of on-boarding a virtual infrastructure management (VIM) server appliance in which VIM software for locally managing a software-defined data center (SDDC) is installed, to enable the VIM server appliance to be centrally managed through a cloud service includes upgrading the VIM server appliance from a current version to a higher version that supports communication with agents of the cloud service, modifying configurations of the upgraded VIM server appliance according to a prescriptive configuration required by the cloud service, and deploying a gateway appliance for running the agents of the cloud service that communicate with the cloud service and the upgraded VIM server appliance.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

Examples described relate to determining a future operation failure in a cloud system. In an example, a historical utilization of resources for performing an operation in a cloud system may be determined. A current utilization of resources in the cloud system may be determined. Based on the historical utilization of resources for performing the operation in the cloud system and the current utilization of resources in the cloud system, a determination may be made whether a future performance of the operation in the cloud system is likely to be a failure. In response to a determination that the future performance of the operation in the cloud system is likely to be a failure, an alert may be generated.

A computing system includes a computing device configured to execute a plurality of virtual machines, each virtual machine of the plurality of virtual machines configured to provide control plane functionality for at least a different respective subset of forwarding units of a network device, the computing device distinct from the network devices. The computing system also includes a policy agent configured to execute on the computing device. The agent is configured to determine that a particular virtual machine of the plurality of virtual machines provides control plane functionality for one or more forwarding units of the network device; determine control plane usage metrics for resources of the particular virtual machine; and output, to a policy controller, data associated with the control plane usage metrics and data associating the particular virtual machine with the one or more forwarding units for which the particular virtual machine provides control plane functionality.

A coordinated network service that facilitates the design and implementation of a coordinated device network of IoT devices. The coordinated network service defines modules for individual IoT devices or coordinated devices that specify the necessary inputs to the device, the outputs from the device and communication protocols. Via an interface, user devices can select a set of IoT devices and specify how they are connected and the decision making logic associated with communication flow. The coordinated network service can then automatically generate mapping information that implements the decision making logic and provides necessary transformations for communications between the specified devices. The selected modules and mappings form a workflow for the coordinated device network. The coordinated network service can then generate executable code to implement the formed workflow in a coordinated device network.

A distributed computing network includes one or more vehicles, each vehicle configured to act as a node in the distributed computing network, and a remote server including a processor and a memory module storing one or more non-transient processor-readable instructions that when executed by the processor cause the remote server to establish a data connection with the one or more vehicles, predict a pattern-of-use of the one or more vehicles, determine a predicted current use of the one or more vehicles, and allocate a computational task to the one or more vehicles based on the predicted pattern-of-use and the predicted current use.

Embodiments of the present disclosure disclose a method and apparatus for acquiring information. The method may include: acquiring cloud resource use information; comparing the cloud resource use information with a use rate threshold value, to obtain a comparison result; obtaining use state information of a cloud resource corresponding to the cloud resource use information according to the comparison result; and generating cloud resource state information based on the use state information.

An information processing method executed by a computer includes: specifying one or a plurality of first physical resources on which virtual resources used by a first user operate; specifying a device connected to the first physical resource and one or a plurality of second physical resources different from the first physical resource, which is connected to the device and on which virtual resources used by a user other than the first user operate; and outputting information that indicates the first physical resource and information that indicates the second physical resource.

A quantum computing service includes connections to multiple quantum hardware providers that are configured to execute quantum circuits using quantum computers based on different quantum technologies. The quantum computing service enables a customer to define a quantum algorithm/circuit in an intermediate representation and select from any of a plurality of supported quantum computing technologies to be used to execute the quantum algorithm/quantum circuit.