Techniques and mechanisms for a host passthrough to be performed based on the execution of a hardware identification instruction with a virtual machine (VM). In an embodiment, a hypervisor process sets a value of a control parameter corresponding to a resource of the VM. The control parameter indicates whether the VM resource is authorized to avail of a host passthrough functionality of a processor which executes the hypervisor process. The control parameter is evaluated, based on a central processing unit identification (CPUID) instruction of a guest operating system which is executed with the VM, to determine whether the CPUID instruction is to result in a host passthrough or a VM exit. In another embodiment, a shared memory resource is searched to determine whether execution of the CPUID instruction is to retrieve information without the use of either the host passthrough or the VM exit.

Examples described herein relate to providing pool-based automated lifecycle management of infrastructure via abstracted resources. According to an example, a consumption model is maintained in which heterogeneous infrastructure is represented in a generalized form as logical resource objects, including nodes and pools of the nodes. The nodes have respective node roles indicative of specific functionality the nodes are operable to provide to a workload based on the respective attributes/qualities of the nodes. A state model is maintained through which the logical resource objects are transitioned among states and responsive to which notifications are provided to an application orchestration tool associated with the workload. Interactions by the application orchestration tool with heterogeneous infrastructure utilized by the workload are abstracted by providing an API through which requests to manage a lifecycle of the heterogeneous infrastructure are expressed with reference to the logical resource objects.

A system and method for management of heterogeneous bare metal servers is disclosed. The system includes target bare metal servers coupled to a network and an administrative server. The administrative server executes a framework for managing the bare metal servers. The framework includes unified APIs for performance of management functions. A recognition engine receives identification information from the server and selects a driver from a driver library based on the identification information. Each of the drivers in the driver library is associated with a different type of bare metal server. A driver loader loads the selected driver to translate the unified APIs to native APIs on the bare metal server to perform the management function on the server.

A system, method, and computer-readable medium are disclosed for servicing and managing a bare metal information handling system. An embedded lightweight operating system on the bare metal information handling system is booted up. The embedded lightweight operating system initiates a platform inference engine which is provided rules and policies as to applications to be run on the bare metal information handling system. The platform inference engine initiates a secure workspace launcher to launch a user workspace user experience environment. The user workspace user experience environment is provided on the bare metal information handling system.

After a terminal receives any piece of control information sent by a management server, if the control information is used to indicate that a foreground operating system of the terminal is a target operating system, the terminal is controlled according to a control instruction carried in the control information. When the foreground operating system of the terminal is not the target operating system, the terminal does not control the terminal according to the control instruction. In addition, the terminal may store a correspondence between a system identifier of the target operating system and the control instruction, so that after the foreground operating system of the terminal is changed to the target operating system, the terminal may further obtain the control instruction based on the correspondence and control the terminal according to the control instruction.

Embodiments described herein are generally directed to a controller of a managed container service that facilitates selection among bare metal machines available within a private cloud. According to an example, a request is received by a Container-as-a-Service controller from a CaaS portal to create a cluster based at least in part on resources of a private cloud of a customer of a managed container service. An inventory of bare-metal machines available within the private cloud is received from a Bare-Metal-as-a-Service (BMaaS) provider associated with the private cloud. A particular bare metal machine is identified for the cluster by selecting among the available bare-metal machines based on cluster information associated with the request, the inventory, and a best fit algorithm configured in accordance with a policy established by the customer.

System and method uses a defined entity type that describes a data structure of a defined computing entity and at least one behavior of the defined computing entity based on user input information. The at least one behavior of the defined computing entity is defined by associating at least one interface with the defined entity type, where the at least one interface represents a reference entity type with a collection of behavior information. An operation is then executed on the defined computing entity according to the at least one behavior of the defined computing entity.

A computer-implemented method for determining whether data is anomalous includes generating a holo-entropy adaptive boosting model using, at least in part, a set of normal data. The holo-entropy adaptive boosting model includes a plurality of holo-entropy models and associated model weights for combining outputs of the plurality of holo-entropy models. The method further includes receiving additional data, and determining at least one of whether the additional data is normal or abnormal relative to the set of normal data or a score indicative of how abnormal the additional data is using, at least in part, the generated holo-entropy adaptive boosting model.

Methods and devices for provisioning a hyper-converged infrastructure of bare metal systems are disclosed herein. Two fabric elements are configured in a master-slave arrangement to ensure high availability. ONIE capable fabric elements may be pre-installed with an operating system as firmware to run open network operating systems, such as Linux. The Linux operating system includes a KVM hypervisor to run virtual machines. An operating system of the virtual machines can access an external network by creating a bridge between switch management ports and a virtual network interface. New node elements may be added by connecting the network ports of the new node element to the fabric elements and booting the new node element in a network/PXE boot mode. The new node element obtains an IP address from a DHCP server and boots an image downloaded from a PXE server.

Selected installed function of a multi-function instruction is hidden such that even though a processor is capable of performing the hidden installed function, the availability of the hidden function is hidden such that responsive to the multi-function instruction querying the availability of functions, only functions not hidden are reported as installed.