430 results about "Orchestration (computing)" patented technology

A scalable platform for orchestrating networks of specialized AI multi-agent networks that enables secure collaboration through token-based protocols and real-time result streaming with advanced dynamic chain-of-thought pruning. The central orchestration engine manages domain-specific agents, implementing sophisticated multi-branch reasoning with contribution-estimation layers that evaluate each agent's utility using Shapley value-inspired metrics. The system employs information-theoretic and gradient-based surprise metric to guide memory updates and dynamic reasoning expansion, preventing local minima stagnation while preserving valuable insights through adaptive forgetting mechanisms. The platform unifies Monte Carlo tree search with contribution-aware estimation to detect high-synergy expert combinations while maintaining privacy through partial data approaches. It scales across distributed computing environments, enabling complex collaborative tasks like materials discovery, product engineering and manufacturing process design, biomedical research, and drug development. The system supports multi-party economic rewards through systematic contribution effort, cost and importance tracking, while standardized interfaces manage security, privacy, and policy constraints across heterogeneous agents.

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

A scalable platform for orchestrating networks of collaborative AI agents utilizing modular hybrid computing architecture. The platform integrates classical, quantum, and neuromorphic computing paradigms through hardware-accelerated translation layers and cross-paradigm coordination mechanisms. A central orchestration engine manages interactions between domain-specific AI agents, dynamically distributing workloads across heterogeneous computing cores based on task complexity, computational requirements, and resource availability. The platform employs hardware-accelerated translation between paradigms, enabling efficient cross-paradigm information exchange while maintaining semantic consistency and computation integrity across different architectures. Specialized monitoring and optimization systems continuously adjust resource allocation and fine-tune performance across computing paradigms. Advanced cache management and fault tolerance mechanisms ensure reliable operation, while privacy-preservation techniques enable secure collaboration. The platform's modular architecture supports integration of different computational approaches, enabling complex multi-domain problem solving that leverages the unique advantages of each paradigm while maintaining system-wide efficiency, scalability, and coherence.

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

A platform for coordinating networks of specialized AI agents that enables secure collaboration through token-based communication and real-time result streaming. The system features a central orchestration engine managing interactions between domain-specific expert agents, with memory management and optional encryption for secure data handling. The platform uses efficient communication protocols for knowledge compression and faster reasoning, while a standardized agent interface system handles security, privacy, and policy requirements. It scales across distributed computing environments to enable complex collaborative tasks like personalized content creation, materials discovery, and drug development while optimizing resource usage and maintaining data privacy.

Various aspects of methods, systems, and use cases for multi-entity (e.g., multi-tenant) edge computing deployments are disclosed. Among other examples, various configurations and features enable the management of resources (e.g., controlling and orchestrating hardware, acceleration, network, processing resource usage), security (e.g., secure execution and communication, isolation, conflicts), and service management (e.g., orchestration, connectivity, workload coordination), in edge computing deployments, such as by a plurality of edge nodes of an edge computing environment configured for executing workloads from among multiple tenants.

The present application relates to the field of information collaborative processing. Provided are a task collaborative scheduling method and apparatus, a device and a medium. The task collaborative scheduling method is applied to a collaborative computing system comprising a plurality of nodes, and the method comprises: first, on the basis of original task description information of a target task, performing splitting and orchestration on the target task to obtain a plurality of sub-tasks and a task logic topological relationship between the sub-tasks; then allocating, on the basis of node information of the nodes, from the collaborative computing system a corresponding execution node for each sub-task, and generating sub-task description information; and finally, issuing the sub-task description information to the execution nodes, such that all the execution nodes can complete all the sub-tasks according to the orchestrated logic topological relationship, thereby obtaining an output result of the target task. The present application can cover diversified task collaborative scheduling scenarios and enables compatibility with access and scheduling of devices having different capabilities, thereby meeting the collaborative processing requirements for diverse service types and scales.

An AI orchestration system dynamically manages multiple artificial intelligence (AI) agents within a cloud computing environment to efficiently process user requests. A model orchestration subsystem determines whether a request is handled locally using a domain-specific database or by invoking one or more AI agents. The system maintains AI agents in active and inactive states, provisioning computing resources for inactive agents as needed. Real-time model metrics guide the selection of target AI agents, and if a degrading performance trend is detected, the system preemptively spins up additional AI instances. The system provisions processor cycles, memory, and network bandwidth through a cloud-based resource manager, instantiates containerized execution environments or virtual machines, and performs automated load balancing among AI instances.

The invention discloses a self-adaptive cloud management platform system based on intelligent resource scheduling and container arrangement, and relates to the field of computer information management. The system comprises a refined resource scheduling and adaptive optimization module, a containerized application life cycle management and dynamic container arrangement module, a high-precision operation and maintenance monitoring and self-healing mechanism module based on big data analysis, and a dynamic resource allocation and elastic scaling strategy module of an intelligent scheduling engine. The system takes a containerization technology as a core, realizes centralized management and monitoring of cloud computing resources, can realize dynamic intelligent resource scheduling and optimization, accelerates application deployment, improves system flexibility, strengthens operation and maintenance monitoring and platform safety guarantee, and comprehensively improves resource optimization and cost effectiveness. The resource scheduling efficiency is improved, the application deployment is simplified, the operation and maintenance monitoring is enhanced, and efficient resource management is realized through an adaptive optimization technology.

The invention relates to the technical field of cloud computing, in particular to an intelligent capacity planning method and system in a hybrid cloud environment. According to the method and the system for intelligent capacity planning in the hybrid cloud environment, heterogeneous resources in a hybrid cloud are modeled by using a declarative description language, a dependency relationship among the resources is identified, and a directed acyclic graph (DAG) is constructed; the resource operation tasks are divided in batches, and the tasks without the dependency relationship are executed in parallel; synchronizing the running state of each resource in real time, and performing state consistency verification and abnormity marking; triggering a retry mechanism when an exception occurs; and after the resource arrangement process is completed, summarizing data in the whole process, and performing performance evaluation and strategy optimization on resource operation. According to the method and the system for intelligent capacity planning in the hybrid cloud environment, the use condition of resources can be monitored and analyzed in real time, the resource demand can be accurately predicted, a reasonable resource scheduling strategy can be generated, manual intervention is reduced, and elastic expansion and optimal configuration of cloud resources are realized.

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

Systems and methods are disclosed for optimization, management, and merging of processes. This system integrates a multifaceted technological framework, including generative artificial intelligence, quantum computing simulations, and blockchain technology. It features a user interface for inputting diverse workflow requirements, a generative AI module for processing these inputs, and a quantum computing module for simulating and optimizing workflows. The system utilizes blockchain for secure workflow deployment and a suite of specialized engines for prompt management, data extraction, analysis, optimization, deployment orchestration, and continuous monitoring. These components ensure the system's adaptability to user-specific needs, scalability across various industries, and capability for integration with existing enterprise systems. This invention revolutionizes BPM by streamlining processes, enhancing efficiency, and maintaining high security and customization standards.

The invention discloses a network management computing power session method and system based on 6G communication, sensing and computing integration, belongs to the technical field of mobile communication, and aims to solve the technical problem of how to realize flexible scheduling and on-demand distribution of computing power and network convergence resources in a 6G communication, sensing and computing integration network environment under the condition of not changing network equipment. A mobile network environment supporting a computing power-as-a-service mode is constructed for a user, and the adopted technical scheme is as follows: the method comprises the following steps of: deploying a computing network integration sensing network element, a computing network integration control scheduling network element and a computing network integration service arrangement network element on a core network side, so that the improved core network has a communication, sensing and computing power integration function; and through analysis and identification of computing power application request indexes, computing power business arrangement, computing power and network Qos configuration mapping, decision making and scheduling of computing network fusion resources, distributed cooperative computing tasks are issued, so that computing power session creation is completed, and different computing power requirements of the mobile terminal are met.

A computing services environment may include a database system storing a plurality of database records for a plurality of client organizations accessing computing services including a conversational chat assistant. The computing services environment may also include an application server may receive user input for the conversational chat assistant, a generative language model interface, an orchestration and planning service configured to identify one or more actions based on the user input, to execute the one or more actions to determine a natural language response message, and to determine a recommended action for selection via a conversational chat interface. The computing services environment may also include a communication interface configured to transmit the natural language response message and a user interface generation instruction executable by the client machine to provide a selection affordance for selecting the recommended action.

A method for data processing by a data clean room orchestration system is described. The method includes receiving an indication of mutually attested code for a data clean room between two or more partners. The method further includes configuring a trusted execution environment (TEE), including one or more virtual machines (VMs) that are individually or collectively operable to execute the mutually attested code. The method further includes transmitting, to endpoints associated with the partners, an attestation report including at least an encrypted token and a host public key of a host machine associated with the one or more VMs. The method further includes receiving respective partner secret keys wrapped with the host public key. The method further includes executing the mutually attested code on respective partner datasets in the TEE based on using a host private key of the host machine to unwrap the respective partner secret keys.

Systems and methods for decentralized orchestration of event records within decentralized computing environments. A processing system including decentralized execution nodes dynamically allocates microservice instances based upon real-time node performance metrics. Transaction requests including transaction data digitally associated with decentralized identifiers (DIDs) are processed by allocated microservice instances. The processing includes verifying transactions, generating cryptographically-linked immutable memorialization records associated with respective DIDs, and distributing these records to a tamper-evident distributed ledger configured to enforce immutability through consensus nodes. The processing system dynamically reallocates microservice instances among decentralized execution nodes based upon monitored utilization metrics and predefined scaling thresholds. Each memorialization record is cryptographically secured and linked permanently to the DID for data immutability, enhanced security, and dynamic scalability for event data processing in distributed infrastructures.

The invention discloses a service-based calculation task dynamic abstraction method and system, and relates to the technical field of calculation task scheduling. The service-based calculation task dynamic abstraction method comprises the following steps: receiving and analyzing a to-be-executed task, and dividing the to-be-executed task into a plurality of independent service units; constructing a directed acyclic graph representing the dependency relationship between the service units, and executing topological sorting based on the directed acyclic graph to determine an execution sequence; and according to the resource demand of each service unit and the current system equipment state. According to the method, the technical problems that a traditional task scheduling method comprises but is not limited to the following technical problems that resource allocation is rigid, a heterogeneous computing environment cannot be dynamically adapted, and the hardware utilization rate is low; parallel arrangement is low in efficiency, depends on manual definition of an execution sequence, lacks an automatic arrangement capability and is difficult to deal with a complex task process; the real-time performance is insufficient, the task execution process is solidified, and the resource allocation and execution path cannot be dynamically adjusted according to the runtime state.

The invention relates to the technical field of cloud computing, in particular to a resource arrangement and automatic execution method and system based on a hybrid cloud architecture. The resource arrangement and automatic execution method based on the hybrid cloud architecture comprises the steps of pulling or receiving monitoring data, performing streaming and batch cleaning, and generating input required by training and reasoning in a feature processing pipeline; the model is deployed, a prediction result is sent to a decision module in real time, and the decision module triggers automatic execution to carry out capacity expansion and shrinkage on resources; and finally, comparing an execution effect with a prediction result to realize continuous optimization. According to the resource orchestration and automatic execution method and system based on the hybrid cloud architecture, cross-cloud resource dependence analysis and task-driven orchestration execution are realized, strategy-driven dynamic deployment and elastic capacity expansion and contraction are also realized, intelligent execution optimization and exception handling can be performed in combination with artificial intelligence or a rule engine, and the resource orchestration and automatic execution method and system based on the hybrid cloud architecture are high in practicability. The resource utilization rate and the operation and maintenance efficiency of the hybrid cloud environment are improved, and the risk of manual operation is reduced.

A hybrid multi-agent orchestration system and methods is proposed where one or more multi-domain primary computational conversation agents are configured to interoperate with a plurality of secondary domain specialized computational conversation agents in query handling. The hybrid terminology refers to a hybrid of primary computational conversation agents and a plurality of secondary domain specialized computational conversation agents that are coupled together in a specific computer architecture designed to enhance the computing capability of both types of agents working together in an effort to improve the accuracy and relevancy of the generated results, albeit at a significantly increased complexity and computational processing cost. The secondary agents can include trained machine learning models having a diversity of data feeds and machine learning architectures.

Systems and methods for managing a computing task are provided. The system includes a controller configured to receive the computing task, present to a client a set of factors defining desired conditions related to executing the computing task, receive a demand computing strategy associated with the computing task indicating one or more factors selected by the client and a constraint and a weight related to each factor from the one or more selected factors, receive a plurality of supply computing strategies from a plurality of datacenters, each supply computing strategy corresponding to a datacenter from the plurality of datacenters, calculate a task scheduling strategy based at least on the demand computing strategy and a supply computing strategy, select a candidate datacenter from the plurality of datacenters according to a predetermined rule, and schedule the computing task for execution on the candidate datacenter according to the task scheduling strategy.

The invention provides a stream computing resource scheduling method and system based on a dynamic time window, and relates to the field of stream computing resource scheduling, and the method comprises the following steps: constructing a master-slave event time monitoring network to obtain a task feature vector, and establishing an adaptive time window scheduling mechanism to dynamically adjust the window size; and constructing a multi-level task priority queue, carrying out state transition by adopting a remote direct memory access mechanism of incremental transmission, and finally coordinating computing nodes through a distributed task arrangement protocol to complete resource switching. According to the method, computing resource competition can be effectively reduced, the flow computing task processing efficiency is improved, dynamic optimal configuration of resources is realized, and the system throughput is enhanced.

The invention relates to a self-adaptive multi-granularity cloud resource arrangement method based on hierarchical reinforcement learning, and belongs to the field of cloud computing resource management. According to the method, an adaptive multi-granularity cloud resource arrangement framework AMG-RL is provided, cloud tasks are adaptively grouped into K task clusters, resource requirements of different task clusters are predicted, and multi-granularity cloud resource arrangement is performed based on the divided different task clusters and the resource requirements of the corresponding task clusters so as to realize task resource allocation and scheduling. And carrying out performance feedback on the multi-granularity cloud resource arrangement strategy so as to adjust and optimize the multi-granularity cloud resource arrangement strategy. The method can adapt to diversity of cloud tasks, reveal similarity and complementarity among the tasks, lay a foundation for differentiated resource allocation, accurately estimate future load change, and maximize resource utilization efficiency and reduce cost while meeting task performance constraints, thereby improving resource utilization efficiency. And the long-term performance of the system is continuously improved through strategy improvement.

A computing services environment may include a database system may store database records for client organizations accessing computing services including a conversational chat assistant. The computing services environment may also include an application server receiving natural language user input for the conversational chat assistant and a generative language model interface providing access to one or more generative language models. The computing services environment may also include an orchestration and planning service configured to analyze the natural language user input via a generative language model of the one or more generative language models to identify a plurality of actions to execute via the computing services environment to fulfill an intent expressed in the natural language user input. The computing services environment may be configured to execute the plurality of actions to determine a natural language response message.

In general, this disclosure describes techniques for leveraging a containerized routing protocol process to implement virtual private networks using routing protocols. In an example, a system comprises a container orchestration system for a cluster of computing devices, the cluster of computing devices including a computing device, wherein the container orchestration system is configured to: deploy a containerized application to a compute node; and in response to deploying the containerized application to the compute node, configure in the compute node a virtual routing and forwarding (VRF) instance to implement a virtual private network (VPN) for the containerized application.

Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

An autonomous job scheduling system for distributed ledger technology is disclosed, leveraging photonic quantum computing and generative artificial intelligence (AI). The system collects transactional and operational data from various nodes within a distributed ledger network, focusing on transaction types, sizes, and priorities. This data is filtered for urgency and resource intensity, analyzed against real-time network conditions and business rules to ascertain job execution parameters. A photonic quantum computing system processes these parameters to optimize job scheduling strategies, including quantum annealing and simulations. The AI engine generates dynamic smart contracts deployed to distributed ledgers by a job orchestration engine. The system monitors job execution, gathering performance data and providing feedback for real-time adjustments. The job orchestration engine updates job scheduling in response to these adjustments, enhancing the efficiency and responsiveness of the blockchain network.

A pod hosted on a node is generated via the cloud orchestration platform. The pod is to be assigned to managing the one or more task processes corresponding to the data set. The pod is designated as a master pod. E ach of one or more worker pods replicates data from the master pod. Each of the one or more worker pods is hosted on another node. An input that triggers an upgrade for the corresponding virtual machines is detected. Execution of a custom script is initiated, which triggers: polling the microservice for status information of the pod and the set of replicas; and determining whether a condition for iteration advancement for the upgrade is satisfied based on the status upgrade. An incremental advancement of the upgrade to a next virtual machine is initiated upon determining that the condition for iteration advancement for the upgrade is satisfied.

Techniques are provided for microservices management and orchestration. A chart package is selectively retrieved from a chart repository based upon the chart package corresponding to a set of services to host within a cluster and dependencies amongst the set of services. A set of container images may be retrieved from a container repository based upon the set of container images corresponding to the set of services. A cluster may be created within a computing environment. The set of services may be deployed as resources of the computing environment within the cluster and the dependencies may be configured using the chart package and the set of container images.

A computing services environment may include a database system storing database records for client organizations accessing computing services including a conversational chat interface, an application server providing access to the conversational chat interface, a metadata repository storing metadata entries characterizing a actions capable of being performed via the computing services environment, and an orchestration service configured to execute an orchestration process based on a natural language request message received via the conversational chat interface. An input prompt including the natural language request message and descriptions of actions selected from the metadata entries may be determined and transmitted to a generative language model. A prompt completion including a plan that includes a subset of the actions and a natural language description of the plan may be received from the generative language model and sent to a client machine via the conversational chat interface.

Methods and systems for managing service deployment are disclosed. To deploy services, dependencies and other characteristics (such as context data, capability data, placement rules, and workflow data) of services and the infrastructure boundaries in which the services are running may be dynamically collected, analyzed and updated. By dynamically analyzing dependencies and other characteristics (such as context data, capability data, placement rules, and workflow data) of services and the infrastructure boundaries, efficiency of use of computing resources may be improved by recycling existing workflow; statically defined operations and hard-coded conditional logic of services may also be avoided each time an environment in which the services are running has changed.