Systems and methods for providing a guaranteed batch pool are described, including receiving a job request for execution on the pool of resources; determining an amount of time to be utilized for executing the job request based on available resources from the pool of resources and historical resource usage of the pool of resources; determining a resource allocation from the pool of resources, wherein the resource allocation spreads the job request over the amount of time; determining that the job request is capable of being executed for the amount of time; and executing the job request over the amount of time, according to the resource allocation.

In general, techniques are described for automatic intent provisioning and management in computer networks. A device comprising a processor, a memory, and an interface may perform the techniques. The processor may obtain a policy that includes high-level configuration data defining a service to be deployed within a network, the high-level configuration data including resource selector criteria that identifies one or more criteria for selecting a resource to support the service from a plurality of potential resources. The processor may also determine, based on the resource selector criteria, the resource to support the service from the plurality of potential resources, and translate the high-level configuration data to low-level configuration data specific to the determined resource. The memory may store the low-level configuration data specific to the determined resource. The interface may enable configuration, when provisioning the service, the determined resource using the low-level configuration data specific to the determined resource.

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.

Systems and methods are described for exporting bucket data from one or more buckets to one or more worker nodes. The system can identify data from different bucket data from buckets stored in a data intake and query system that is to be processed by one or more worker nodes. The system can allocate one or more execution resources, such as a processing pipeline, to process and export the bucket data from the buckets. The system can assign bucket data corresponding to individual buckets to the execution resource based on a bucket distribution policy. The indexer can export the bucket data to the worker nodes for further processing based on the bucket data-execution resource assignment.

Embodiments of the disclosure relate to configuring a watermark unit with watermark algorithms for artificial intelligence (AI) models for a data processing (DP) accelerator. In one embodiment, in response to a request received by a DP accelerator, the request, sent by an application, to apply a watermark algorithm to an AI model by the DP accelerator, a system determines that the watermark algorithm is not available at a watermark unit of the DP accelerator. The system sends a request for the watermark algorithm. The system receives the watermark algorithm by the DP accelerator. The system configures the watermark unit at runtime with the watermark algorithm for the watermark algorithm to be used by the DP accelerator.

A request manager analyzes API calls from a client to a host application for state and performance information. If current utilization of host application processing or memory footprint resources exceed predetermined levels, then the incoming API call is not forwarded to the application. If current utilization of the host application processing and memory resources do not exceed the predetermined levels, then the request manager quantifies the processing or memory resources required to report the requested information and determines whether projected utilization of the host application processing or memory resources inclusive of the resources required to report the requested information exceed predetermined levels. If the predetermined levels are not exceeded, then the request manager forwards the API call to the application for processing.

Computing node identifiers can be used to encode information regarding the distance between requesting and available computing nodes. Computing node identifiers can be computed based on proximity values for respective computing nodes. Requests can be directed from one computing node to an available computing node based on information encoded by both the computing node identifiers of the requesting node and the receiving node. Using these computing node identifiers to direct request traffic among VMs can more efficiently leverages network resources.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

The present disclosure provides a method and system for selecting an optimal edge computing node in an Internet of vehicle (IoV) environment. The method includes: acquiring and analyzing properties of computing tasks of a vehicle in the IoV environment; acquiring and analyzing properties of different edge computing nodes; computing matching degrees between the properties of the computing tasks and the properties of the nodes; analyzing computing demands of different tasks, and assigning weights to different types of matching degrees; and selecting a node having an optimal sum for products of the matching degrees and the weights as an optimal edge computing node to compute each of the computing tasks of the vehicle.

Disclosed are a network accuracy quantification method, system, and device, an electronic device and a readable medium, which are applicable to a many-core chip. The method includes: determining a reference accuracy according to a total core resource number of the many-core chip and the number of core resources required by each network to be quantified, with the number of the core resources required by each network to be quantified being the number of the core resources which is determined after each network to be quantified is quantified; and determining a target accuracy corresponding to each network to be quantified according to the reference accuracy and the total core resource number of the many-core chip.