Disclosed herein is the integration into edge nodes of a telecommunications network system of client computer system and server computer system where the server computer system includes a pool of shareable accelerators and the client computer runs an application program that is assisted by the pool of accelerators. The edge nodes connect to user equipment, and some of the user equipment can themselves act as one of the client computer systems. In some embodiments, the accelerators are GPUs, and in other embodiments, the accelerators are artificial intelligence accelerators.

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.

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.

Generating compilation methods using sustainability. Code is compiled in a manner that accounts for sustainability values. When a compilation request is received, sustainability values are identified. The resources needed to fulfill the compilation request are identified based on the sustainability values and available resources. Once the resources that are likely to best meet the sustainability values, the compilation request is performed using those resources.

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 and software resources to provide the desired computer implemented services. To improve the likelihood of the computer implemented services being provided, the managed devices 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 take into account both historic use of managed systems and changes to subscriptions to ascertain point in time when subscription limits may be reached. The identified points in time 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 system and method for multi-region deployment of application jobs in a federated cloud computing infrastructure. A job is received for execution in two or more regions of the federated cloud computing infrastructure, each of the two or more regions comprising a collection of servers joined in a raft group for separate, regional execution of the job generating a copy of the job for each of the two or more regions. The job is then deployed to the two or more regions, the workload orchestrator deploying the job according to a deployment plan. A state indication is received from each of the two or more regions, the state indication representing a state of completion of the job by each respective region of the multi-cloud computing infrastructure.

Examples of a method for brokering remote servers are described herein. In some examples, performance data is received from a plurality of remote servers, where the performance data indicates rendering performance of a foreground application executed by at least one of the remote servers and streamed from at least one of the remote servers over a remote desktop connection. An indication of a selected application is received from a client. The client is directed to at least one of the remote servers based on the performance data and the selected application.

A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.

An apparatus includes a processor and a memory that stores a deep Q reinforcement learning (DQN) algorithm configured to generate an action, based on a state. Each action includes a recommendation associated with a computational resource. Each state identifies at least a role within an enterprise. The processor receives information associated with a first user, including an identification of a first role assigned to the user and computational resource information associated with the user. The processor applies the DQN algorithm to a first state, which includes an identification of the first role, to generate a first action, which includes a recommendation associated with a first computational resource. In response to applying the DQN algorithm, the processor generates a reward value based on the alignment between the first recommendation and the computational resource information associated with the first user. The processor uses the reward value to update the DQN algorithm.