Techniques for optimizing virtual machine (VM) scheduling on a non-uniform cache access (NUCA) system are provided. In one set of embodiments, a hypervisor of the NUCA system can partition the virtual CPUs of each VM running on the system into logical constructs referred to as last level cache (LLC) groups, where each LLC group is sized to match (or at least not exceed) the LLC domain size of the system. The hypervisor can then place/load balance the virtual CPUs of each VM on the system’s cores in a manner that attempts to keep virtual CPUs which are part of the same LLC group within the same LLC domain, subject to various factors such as compute load, cache contention, and so on.

A disclosed workload distribution method determines a yield index for each microservice associated with a containerized application executing on a potentially heterogeneous cluster information handling systems. Each microservice is then assigned to one of N priority categories based on its yield index, where three is an acceptable, but not exclusive, value of N. Resource configuration profiles are maintained for each of the priority categories. Each resource configuration profile assigns a resource configuration to each microservice. An information handling resource associated with a particular microservice is configured in accordance with the resource configuration assigned to the particular microservice by a particular resource configuration profile corresponding to the yield index. In this manner, workloads can be assigned and resources configured in accordance with the containerized application's priorities as exposed by the value indices.

In general, the invention relates to providing computer implemented services using information handling systems. One or more embodiments of the invention includes identifying a hardware resource requirement in a composition request for a composed information handling system, wherein the hardware resource requirement specifies a hardware resource with data transformation functionality (DTF), identifying a hardware resource that does not have the DTF, connecting the hardware resource to a DTF container, wherein the DTF container implements the DTF and emulates the hardware resource with DTF, and initiating composition of the composed information handling system using the DTF container, wherein the DTF container satisfies the hardware resource requirement.

A processing unit is configured differently based on an identified workload, and each configuration of the processing unit is exposed to software (e.g., to a device driver) as a different virtual processing unit. Using these techniques, a processing system is able to provide different configurations of the processing unit to support different types of workloads, thereby conserving system resources. Further, by exposing the different configurations as different virtual processing units, the processing system is able to use existing device drivers or other system infrastructure to implement the different processing unit configurations.

The one or more programs stored in a computer-readable storage medium according to various embodiments include instructions cause the first electronic device to receive, from a third electronic device, a first signal related to a user input to access a character corresponding to a user of the third electronic device through a first connection between the first electronic device and the third electronic device, establish a second connection between the first electronic device and a second electronic device distinct from the third electronic device and third connection for transmission of graphic data from the third electronic device to the second electronic device, transmit a second signal for requesting generation of the graphic data including the character controlled based on the second user input to the second electronic device through the second connection, and control the character independently of the transmission of the graphic data based on the third connection.

A method, system, and computer program product for automated increment analysis of legacy applications are provided. The method receives a set of service properties for a service to be generated from a set of applications. The set of applications are associated with a set of resources. A subset of resources are determined based on the set of service properties. The subset of resources are to be included in the service. A resource graph of the subset of resources is generated based on the subset of resources and the set of service properties. The method generates a service increment including at least a portion of the subset of resources based on the resource graph and the set of service properties.

Methods, systems, and devices for providing computer implemented services using managed systems are disclosed. To provide the computer implemented services, the managed systems may need to operate in a predetermined manner conducive to, for example, execution of applications that provide the computer implemented services. Similarly, the managed system may need access to certain hardware resources (e.g., and also software resources such as drivers, firmware, etc.) to provide the desired computer implemented services. To improve the likelihood of the computer implemented services being provided, the managed systems may be managed using a subscription based model. The subscription model may utilize a highly accessible service to facilitate system management. To facilitate system management, the highly available service may utilize various types of reporting models to identify use of the managed systems. The identified use may be used to drive management decisions.

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.

A method, a device, and a non-transitory storage medium are described in which an elastic platform virtualization service is provided in relation to a virtual device. The elastic platform virtualization service includes logic that provides for the management of a virtualized device during its life cycle. The creation or reconfiguration of the virtualized device is based on a tertiary choice between using dedicated hardware and dedicated kernel; common hardware and common kernel; or a combination of the dedicated hardware, dedicated kernel, common hardware, and common kernel.

An apparatus for executing a software program, comprising processing units and a hardware processor adapted for: in an intermediate representation of the software program, where the intermediate representation comprises blocks, each associated with an execution block of the software program and comprising intermediate instructions, identifying a calling block and a target block, where the calling block comprises a control-flow intermediate instruction to execute a target intermediate instruction of the target block; generating target instructions using the target block; generating calling instructions using the calling block and a computer control instruction for invoking the target instructions, when the calling instructions are executed by a calling processing unit and the target instructions are executed by a target processing unit; configuring the calling processing unit for executing the calling instructions; and configuring the target processing unit for executing the target instructions.