220 results about "Orchestration" patented technology

Some implementations of the disclosure provide a computer-implemented method including operations of receiving, by an orchestration agent, user input corresponding to a user question, wherein generating a response to the user question includes one of generating, editing, or refining programming code, and generating, by the orchestration agent, a prompt instructing a first sub-large language model (LLM) to perform a first task. In response to the prompt, generating, by the first sub-LLM, instructions for a second sub-LLM to perform a second task, wherein results of performing the second task by the second sub-LLM are provided to the first sub-LLM and performing, by the first sub-LLM, the first task utilizing the results of the second task generated by the second sub-LLM. An additional operation includes generating, by the orchestration agent, a GUI that displays the response to the user question, wherein the response includes or is based on the programming code.

A system for context-sensitive orchestration of autonomous agents in cloud platforms, consisting of: a hardware-based orchestration device configured for integration into a distributed cloud infrastructure; a context inference engine within the orchestration device, wherein the context inference engine is configured to receive and aggregate real-time telemetry data from a variety of distributed nodes, including at least one system-level parameter, at least one application-level parameter, and at least one environment parameter; a semantic inference module within the context inference engine, configured to generate a context-related state representation by correlating the parameters using a knowledge graph-based model of interdependencies; an optimization unit for machine learning within the orchestration device, which is communicatively connected to the context inference engine and is configured to predict resource requirements and operational states using reinforcement learning models trained on historical and real-time data streams; a policy-driven orchestration controller configured to translate the contextual state representation into actionable orchestration decisions by applying dynamic orchestration policies stored in a domain-specific policy repository; and a distributed agent interaction bus configured to delegate orchestration decisions to a variety of autonomous agents deployed on the cloud platform.

Systems and methods for hierarchical workspace orchestration are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a processor and a memory, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive first one or more files from a workspace orchestration service, where the first one or more files are usable by the IHS to instantiate a first workspace; and provide second one or more files from the first workspace to a first peripheral device, where the second one or more other files are usable by the first peripheral device to instantiate a second workspace, where the second workspace is configured to provide third one or more files to a second peripheral device coupled to the first peripheral device, and where the third one or more files are usable by the second peripheral device to instantiate a third workspace.

The present application provides a message-driven microservice dynamic orchestration method and related equipment. The method is applied to a microservice dynamic orchestration system, wherein the system includes multiple microservices, and the method includes: obtaining the impact factor of the microservice from the business message; generating the process orchestration data of the microservice according to the message subject preset by the microservice and the impact factor, and storing the process orchestration data in a cache center; allowing each of the microservices in all the microservices to read the process orchestration data from the cache center and determine the next microservice connected to it according to the process orchestration data. The method provided by the present application realizes the dynamic orchestration of microservices, and there is no need to change or restart the microservice when adjusting the microservice call link, thereby ensuring the work efficiency of the system, reducing the difficulty of system maintenance, and solving the problem that the microservice call link is difficult to adjust.

A system for latency-aware orchestration and performance optimization in AI-driven telephone communication, consisting of: a speech capture unit configured to capture an analog audio signal from a telephone interface and convert the analog audio signal into a digital audio signal stream; a feature extraction unit that is operationally coupled with the speech acquisition unit and is configured to generate a feature representation of the digital audio signal stream through spectral decomposition, noise reduction, and temporal segmentation; an AI inference processor communicatively connected to the feature extraction unit, configured to run one or more AI models for automatic speech recognition, natural language understanding, and emotion recognition on the feature representation to generate intermediate results for inference; a latency orchestration controller coupled to the AI ​​inference processor, wherein the latency orchestration controller is configured to monitor latency across multiple processing stages, predict cumulative delay propagation using a hybrid latency estimation model, and orchestrate the execution scheduling of the AI ​​inference processor based on the predicted latency deviation; a performance optimization unit coupled with the latency orchestration controller and configured to dynamically adjust computational accuracy, inference batch size, and feature processing resolution based on latency thresholds and quality constraints set by the latency orchestration controller; and a transmission synchronization array configured to time-align the processed output generated by the AI ​​inference processor and transmit it to a remote communication node, with the transmission synchronization array maintaining deterministic time coordination between successive packets and the orchestrated inference results.

Systems and methods for user steering through workspace orchestration are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the IHS to: receive, at a local management agent from a workspace orchestration service, one or more files configured to enable the local management agent to instantiate a workspace based upon a workspace definition, where the workspace definition enables the workspace to communicate with a selected peripheral device.

The invention relates to the technical field of software development, and provides a dynamic arrangement type invoicing method and system, and the method comprises the steps: building a code-level atomic component library; packaging a corresponding flow node component for each atomic component in the flow editor, and providing a drag-and-drop atomic component arrangement flow interface; the atomic component arrangement process precondition verification is realized; generating a self-defined DSL of an atomic component arrangement process; the invoicing process executor is responsible for analyzing the DSL and calling and executing the atomic component according to an arrangement process; a local invoicing tagging interface and function are realized, an invoicing process executor is called, and an invoicing transaction is closed. The invention provides an invoicing mode which supports online editing and is based on capability arrangement, rearrangement of the invoicing transaction process can be dynamically realized only by adjusting the arrangement process of the atomic component online by utilizing the draggable process editing interface, and diversified configuration requirements of the invoicing transaction process are effectively met.

Systems and methods for dynamic power limit orchestration in a firmware framework are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform with multiple devices, and an orchestrator with firmware that, upon execution by a processing core, causes the processing core to communicate with each of the plurality of devices to obtain a power consumption level for the device, and adjust a power limit value of the IHS according to a cumulative power consumption level of the devices.

Systems and methods for workspace orchestration based on energy scores are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a processor; and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the IHS to: receive, at a local management agent from a workspace orchestration service, one or more files configured to enable the local management agent to instantiate a workspace, where the one or more files are selected or assembled based upon a workspace definition created, at least in part, based upon an energy score; and instantiate the workspace.

Systems, processes, and apparatuses are disclosed including an orchestration agent and an orchestration hub. The orchestration agent is operatively connected to an eUICC and memory at a mobile computing device. The orchestration hub includes a processor and a database including centralized network subscription information. The orchestration hub can receive updated network subscription information from a device management and from a network connectivity management platform. The orchestration hub can compare the updated network subscription information to a plurality of subscription records in the centralized network subscription information. The orchestration hub generates a new subscription record including current network subscription information based on the updated network subscription information. The orchestration agent can replace one or more information fields of a subscription edge record at the eUICC and the memory with corresponding information fields from the new subscription record.

Techniques are disclosed relating to implementing an end-to-end orchestration for a datacenter on a cloud platform. A datacenter may be orchestrated on a cloud platform according to a declarative specification that describes dependences between datacenter entities (e.g., services) in the datacenter. Some datacenter entities may execution dependencies, which include activities, steps, or events that need to be completed for the datacenter entities to be ready for orchestration. Accordingly, in order for an orchestration workflow to be able to execute from beginning to end without interruption, all the execution dependencies for all the datacenter entities in the orchestration workflow need to be completed. The techniques disclosed include automatically initiating execution of the execution dependencies and waiting for indications that the execution activities are completed before executing the orchestration workflow.

The invention relates to the technical field of multi-scene dynamic orchestration, in particular to a multi-scene-based orchestrable unified preprocessing rule method and system, and the method comprises the steps: carrying out the dynamic matching of a rule according to scene information, employing a visual rule orchestration engine, and employing a micro-service architecture design; the method has the beneficial effects that different service scenes can be covered through the modular rule base and the dynamic configuration capability, repeated development of a traditional single scene preprocessing system is avoided, and the technology reuse rate is improved. Visual flow arrangement is adopted, a user is allowed to customize a rule execution sequence, conditional branches and combinatorial logic, complex business demand changes are flexibly dealt with, and the technology coupling degree is reduced.

Computer systems and methods with decentralized blockchain orchestration architecture use multiple orchestrators embedded at the cluster level. A push architecture is provided for manipulating and verifying workload configurations, including the ability to determine differences between stored configurations and actual configurations.

Techniques are described for performing an automated region build. An orchestration service (e.g., a Multi-Flock Orchestrator (MFO)) may be configured to bootstrap any suitable number of services within a region corresponding to one or more data centers. Each service can be associated with a respective set of resources (e.g., infrastructure components to be provisioned, software artifacts to be deployed, etc.). The WO can obtain configuration files corresponding to the services to be bootstrapped and perform a static analysis the configuration files to identify one or more dependencies between the services. Circular dependencies can be identified and resolved before region build. A graph may be generated that maintains the dependencies identified and indicates a corresponding order with which bootstrapping tasks are to be performed. The WO may traverse the graph to incrementally instruct, according to the identified dependencies, a provisioning and deployment manager to bootstrap services in the region.

The invention provides a service orchestration method and device and related equipment, and the method comprises the steps: obtaining a first state space and a first action space of a to-be-orchestrated service, the first state space being used for representing the states of a server and an interface related to the to-be-orchestrated service, and the first action space being used for representing the states of the server and the interface related to the to-be-orchestrated service; the first action space is used for representing an interface function and a calling relation of an interface related to the to-be-arranged service; based on the first state space and the first action space, generating first calling information of an interface related to the to-be-arranged service; based on the first action space, M candidate interface calling sequences of the to-be-arranged service are generated, and M is a positive integer; selecting a target interface calling sequence matched with the first calling information from the M candidate interface calling sequences; and performing link configuration on the to-be-arranged service based on the target interface calling sequence.

The invention discloses a cross-culture melody feature extraction and musical instrument matching system based on deep learning, and belongs to the technical field of music information processing and artificial intelligence. The system comprises an audio acquisition module, an audio preprocessing module, a melody feature extraction module, a cultural context understanding module, an emotion semantic analysis module, a musical instrument timbre database, a musical instrument matching recommendation module, a distributor scheme generation module, a user interaction module and an audio synthesis module. Multi-dimensional melody features are extracted through a multi-scale convolutional neural network and a bidirectional long-short term memory network, cultural semantic understanding is realized through reasoning on a music knowledge graph by using a graph neural network, and musical instrument matching is performed by using a multi-objective optimization algorithm in combination with three dimensions of timbre integrating degree, cultural consistency and emotional expressive power. According to the system, automation of the whole process from humming melody to musical instrument configuration is achieved, the efficiency of the instrument is improved by 400%, the accuracy rate reaches 92%, and an intelligent tool is provided for cross-culture music creation and national music modernization reorganization.

The invention provides a micro-service deployment and arrangement optimization method and system for a deep neural network, and the method comprises the steps: enabling a cloud center node to deploy a plurality of micro-services composed of different container mirror images to a plurality of edge server nodes based on the micro-service deployment starting time and resource conditions, each micro-service is constructed based on a service branch of a pre-trained multi-branch deep neural network model, and each service branch corresponds to a micro-service set; determining a deployment and arrangement joint optimization target based on micro-service deployment starting time, service time delay and service precision, and performing joint iterative optimization including micro-service deployment optimization and micro-service arrangement optimization according to the deployment and arrangement joint optimization target. And when the result of the joint iterative optimization cannot achieve the deployment and arrangement joint optimization target, further performing arrangement iterative adjustment based on multi-branch deep neural network model branch optimization to achieve the deployment and arrangement joint optimization target.

The invention relates to the technical field of virtualization orchestration, in particular to an industrial control virtualization orchestration system and method, and the system comprises a task extraction module, a node behavior monitoring module, a stability evaluation module, an orchestration load adjustment module and a control task migration module. According to the method, the scheduling cycle closed-loop feature recognition is performed by collecting the control task scheduling mapping table and the execution instruction queue and combining the cycle control mark and the interrupt mark, so that an orchestratable task set can be effectively recognized, the behavior success rate is calculated according to parameters such as node response delay and task completion proportion, and node stability screening is performed; stability grade division of control nodes is achieved, a scheduling priority list is formed by setting a release threshold value, then the task migration opportunity is judged based on task residence time and IO completion conditions, migration targets are accurately selected in a behavior pattern matching mode, and the stability of task scheduling, the precision of resource allocation and the adaptability of control logic are remarkably improved.

Techniques are disclosed relating to implementing automated retries during orchestration of a datacenter on a cloud platform. Generating an orchestration workflow for the datacenter may include generating an aggregate pipeline for the orchestration. The aggregate pipeline includes instances of datacenter entity pipelines that include stages for provisioning and deployment of datacenter entities. The disclosed techniques include adding retry stages to the datacenter entity pipelines that are automatically invoked in the event of failure of a datacenter entity pipeline. The retry stages are placed at the end of individual datacenter entity pipelines and conditional expressions are included that invoke retry strategies defined by owners of the datacenter entity.

Various systems and methods for implementing intent-based orchestration in heterogenous compute platforms are described herein. An orchestration system is configured to: receive, at the orchestration system, a workload request for a workload, the workload request including an intent-based service level objective (SLO); generate rules for resource allocation based on the workload request; generate a deployment plan using the rules for resource allocation and the intent-based SLO; deploy the workload using the deployment plan; monitor performance of the workload using real-time telemetry; and modify the rules for resource allocation and the deployment plan based on the real-time telemetry.

Techniques are described for performing an automated region build. An orchestration service (e.g., a Multi-Flock Orchestrator) may managing bootstrapping efforts for any suitable number of services within a region corresponding to one or more data centers. Each service can be associated with a respective set of resources (e.g., infrastructure components, software artifacts, etc.). The orchestration service can identify dependencies between services based on corresponding configuration files associated with the services. The orchestration service can determine, based on the identified dependencies, an order by which operations for bootstrapping the services are to be performed. The orchestration service can incrementally instruct other system components (e.g., a provisioning and deployment manager) to perform operations for bootstrapping the plurality of services in accordance with determined order.

Long-term memory data objects may be generated for orchestrations agents. When a session completes or ends, a long-term memory data object may be generated according to a specified long-term memory type based on turn inputs during the session. When a new session is started, the long-term memory data object may be used as part of inputs to a generative machine learning model to perform or respond to turn inputs of the new session.

Various systems and methods for reactive intent-driven end-to-end (E2E) orchestration are described herein. An orchestrator system, includes a processor; and memory to store instructions, which when executed by the processor, cause the system to: receive, at the orchestrator system, an intent-based service level agreement (SLA) for execution of a series of tasks on a plurality of compute nodes; calculate, based on the intent-based SLA, intermediate latency thresholds corresponding to each task of the series of tasks; calculate slack estimates based on the latency thresholds and real-time telemetry of the plurality of compute nodes or real-time telemetry of connections between the plurality of compute nodes; monitor execution of the series of tasks on the plurality of compute nodes; and perform a corrective action in response to determining that the execution of the series of tasks is predicted to exceed one of the intermediate latency thresholds.

Approaches presented herein provide for the management of resources to be used to process a request, such as may involve orchestration of nodes for an inference request. Upon receiving an inference request, an orchestrator can determine a sequence of context nodes and inference nodes to be used to process the inference request, based in part upon a determined class of inferencing to be performed. The orchestrator can append metadata to the inference request that identifies the sequence, and can transmit the appended request to one or more first nodes in the sequence. If the nodes have a network programmable device, or similar capability, the request can be forwarded to the nodes in sequence without having to go back to the orchestrator between nodes.

Systems and methods for providing trusted local orchestration of workspaces are described. In some embodiments, an information handling system (IHS) can include: a processor; and a system memory coupled to the processor, the system memory having program instructions stored thereon that, when executed, cause the IHS to: receive, from a workspace orchestration service, orchestration code; use a trusted controller coupled to the processor to record a log, the log including: the orchestration code, and an indication of a sequence of operations performed by a local management agent during instantiation of a workspace; provide, to the workspace orchestration service, a copy of the log; and in response to the workspace orchestration service successfully: (i) authenticating the orchestration code, and (ii) verifying the sequence of operations, establish a connection between the workspace and the workspace orchestration service.

Systems, processes, and apparatuses are disclosed including an orchestration agent and an orchestration hub. The orchestration agent is operatively connected to an eUICC and memory at a mobile computing device. The orchestration hub includes a processor and a database including centralized network subscription information. The orchestration hub can receive updated network subscription information from a device management and from a network connectivity management platform. The orchestration hub can compare the updated network subscription information to a plurality of subscription records in the centralized network subscription information. The orchestration hub generates a new subscription record including current network subscription information based on the updated network subscription information. The orchestration agent can replace one or more information fields of a subscription edge record at the eUICC and the memory with corresponding information fields from the new subscription record.

A system and method are disclosed for generating personalized motivational, wellness, educational, and entertainment multimedia content using multimodal artificial intelligence. The system constructs an Emotion State Vector (ESV) from user inputs, diaries, biometric data, and context. A Modality Script Generator (MSG) defines time-coded orchestration across music, narration, captions, and visuals. Scripts execute via a Model-Agnostic Adapter Layer (MAAL) across heterogeneous Al engines. A Synchronization Graph (SyncGraph) maintains acceptable inter-modal drift, while a Dynamic Media Formatter (DMF) applies bounded repair policies and packages synchronized outputs with timed metadata. A Policy / Constraint Graph enforces rights and provenance, logged in a Transform Manifest.

The embodiment of the invention provides a method and system for generating an orchestration program, electronic equipment and a storage medium, and the method comprises the steps: obtaining a first to-be-orchestrated service; configuring common information of the first to-be-orchestrated service based on an orchestration engine, and generating an initial orchestration program; and on the basis of the initial arrangement program, the difference information of the first to-be-arranged service is configured based on the protocol stack, and a target arrangement program is generated to realize arrangement of the first to-be-arranged service. According to the method and the device, the technical problems of low development efficiency and low orchestration program reuse degree caused by the fact that different orchestration services need to repeatedly develop the same function in the orchestration system development process are solved.

Computer systems and methods with decentralized blockchain orchestration architecture use multiple orchestrators embedded at the cluster level. A push architecture is provided for manipulating and verifying workload configurations, including the ability to determine differences between stored configurations and actual configurations.

A cloud infrastructure orchestration service (CIOS) may track build progress by any suitable number of area orchestrators. The orchestrator control plane may be configured to generate an area build plan for guiding a plurality of services within the data center. The orchestrator control plane may instruct the area orchestrator to perform the build according to the build plan. The region orchestrator may be configured to update an execution state corresponding to execution of the region construction plan when it executes a step of the ordered steps of the region construction plan. Intervention data may be received at any suitable time (e.g., when performing one of the steps fails), using which new region building plans may be generated. The new region build plan may be used for subsequent region build execution. This may enable runtime correction.