182 results about "Orchestration (computing)" patented technology

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.

In some aspects, a cloud-based computer system includes: a quantum computing system comprising a quantum processing unit; a container management and execution system configured to receive a container and execute a program within the container; and a communication channel between the container management and execution system and the quantum computing system for providing program instructions to the quantum computing system. The container management and execution system and the quantum computer system may be co-located in a data center or located in different data centers. The latency of the communication channel may be selected to optimize cost for a required computer performance.

The invention discloses an airborne environment-oriented cloud native resource scheduling deployment control system and method, and belongs to the field of implementation and technical development of a cloud native architecture in an airborne environment. Comprising a heterogeneous resource definition template, a heterogeneous resource module management component, a heterogeneous resource agent component, a heterogeneous resource controller component, a heterogeneous resource scheduler component and an interface server. Performing selected type heterogeneous resource definition by adopting a heterogeneous resource definition template; creating a resource instance in the interface server; the heterogeneous resource controller component performs resource control tuning; creating a function application instance in the interface server; the heterogeneous resource scheduler component makes a scheduling decision; the heterogeneous resource agent component reads the function deployment information and issues the function deployment information to the heterogeneous resource module management component; and the heterogeneous resource module management component performs program loading and unloading operation and information updating and reporting. According to the invention, the adaptability and operation efficiency of the container scheduling engine in the airborne heterogeneous computing resource environment can be improved.

A technique manages computing resources on a container orchestration platform. Such a technique involves establishing a pool of computing resources on the container orchestration platform. Such a technique further involves, after the pool of computing resources is established, receiving graphics processing unit (GPU) provisioning requests (GPRs) which identify workspaces. Such a technique further involves allocating computing resources from the pool to the workspaces identified by the GPRs based on a set of GPR prioritization policies.

The invention belongs to the technical field of container orchestration, and provides an intelligent scheduling method, device, equipment, medium and product of a container orchestration system.According to the method, cluster operation data of a member cluster in a single-cluster architecture or a multi-cluster architecture deployed by the container orchestration system is obtained, and the member cluster is composed of multiple nodes; the nodes are servers or computing units for bearing Pod operation of the container group, and the cluster operation data comprises node resource utilization rate, Pod operation performance, application performance indexes and load change data; in response to a received scheduling request of the Pod to be scheduled, screening a plurality of candidate nodes meeting Pod operation constraint conditions from the member cluster; determining a target node in the plurality of candidate nodes based on the cluster operation data, the feature information of the to-be-scheduled Pod and a preset scheduling strategy for calling an AI inference engine; binding the Pod to be scheduled to a target node, and monitoring operation state data after the Pod is bound to the target node; and optimizing a decision model of the AI inference engine and a preset scheduling strategy based on the operation state data to form a scheduling closed loop.

A computing services environment may include application servers providing computing services including access to a database system, a unified metadata framework including autonomous agent definitions referencing action definitions defining a plurality of actions capable of being performed within the computing services environment, an agent service configured to instantiate an autonomous agent instance based on an autonomous agent definition, and an orchestration layer configured to determine an orchestration plan based on novel planning text generated by a generative language model. The orchestration plan may include a subset of the plurality of actions identified in the novel planning text. The computing services environment may execute the subset of the plurality of actions within the computing services environment.

The invention relates to a shared GPU resource fair scheduling method and system based on multi-dimensional priority, and belongs to the field of cloud computing and container orchestration.The method comprises the steps that pooling and virtualization segmentation are conducted on physical GPU resources in a cluster, and a unified GPU resource pool is formed; calculating the comprehensive priority weight of the task according to the user level, the task type and the service quality in combination with a multi-dimensional priority model; a scheduling decision is made based on a weighted dominant resource fair algorithm, task priority weights and dominant resource proportions are comprehensively considered, and fair scheduling and optimal distribution of GPU resources are achieved; through a resource monitoring and feedback mechanism, the GPU utilization rate, the task execution state and the resource allocation result are monitored in real time, priority parameters and scheduling weights are dynamically adjusted, and finally fair scheduling of shared GPU resources is achieved. According to the method, intelligent allocation, resource sharing and fair scheduling of tasks can be realized in a GPU virtualization environment, and the scheduling efficiency and the overall cluster utilization rate are improved.

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.

Aspects of the disclosure relate to computing platforms that utilize machine learning to perform output generation based on intent identification. The computing platform may train intent orchestration models (e.g., intent identification, output generation, or communication channel) using historical data. The computing platform may data corresponding to an individual. Based on the data, the computing platform may select intent identification models, and may use them to identify an intent. Based on the intent of the individual, the computing platform may select engagement output generation models, and may use them to generate a customer engagement output. The computing platform may use a communication channel model to identify a communication channel. The computing platform may send commands directing display of the customer engagement output, which may cause a user device to display the customer engagement output using the communication channel.

A method and apparatus for dynamic orchestration of distributed artificial intelligence in a network including a user device, an edge server, and a cloud server. The method includes receiving, at the user device, input data comprising at least one of audio, video, image, or text; identifying a requested operation based on the input data; obtaining dynamic environmental information of the network relating to computing resources and network conditions of the user device and at least one of the edge server or the cloud server; determining, based on the requested operation and the dynamic environmental information of the network, a distributed allocation of the requested operation among the user device, the edge server, and the cloud server; and orchestrating the requested operation according to the distributed allocation.

A computing services environment may include application servers providing computing services including access to a database system, a unified metadata framework including autonomous agent definitions referencing action definitions defining a plurality of actions capable of being performed within the computing services environment, an agent service configured to instantiate an autonomous agent instance based on an autonomous agent definition, and an orchestration layer configured to determine an orchestration plan based on novel planning text generated by a generative language model. The orchestration plan may include a subset of the plurality of actions identified in the novel planning text. The computing services environment may execute the subset of the plurality of actions within the computing services environment.

A scalable orchestration framework including an orchestrator platform is disclosed. The orchestration platform is configured to: (i) in response to receiving a first request data message, identify a total number of services of a plurality of value-added services to be invoked at a respective plurality of value-added services platforms; (ii) determine permissioned data elements of a plurality of data elements of the first request data message to be provided to each of the invoked value-added services; (iii) instantiate a service execution layer corresponding to each service of the plurality of services; (iv) provision each instantiated service execution layer with the permissioned data elements for the corresponding service of the plurality of invoked services; and (v) generate and transmit, to the requestor computing device, a first response data message based upon a received service response message from each instantiated service execution layer.

The invention discloses a virtual-real combined sky-ground multi-domain heterogeneous network cooperative computing simulation system and a virtual-real combined sky-ground multi-domain heterogeneous network cooperative computing simulation method. The system comprises a heterogeneous computing power prototype subsystem, a virtual simulation subsystem, a satellite-borne general computing terminal, a programmable network simulator, a heterogeneous resource management subsystem and a simulation verification system interface. Real hardware computing power characteristics are introduced through a physical board card, large-scale expansion is realized through virtual nodes, and authenticity and large-scale performance of simulation are considered; link parameters are generated according to a high-precision orbit dynamics model, and the fidelity of sky-ground network simulation is greatly improved; based on a mixed container arrangement technology, unified management and intelligent scheduling of sky, air and ground heterogeneous resources are realized, and a powerful platform is provided for verifying an advanced cross-domain collaborative algorithm; one set of system can support various simulation verification requirements of networking routing, task unloading, distributed cooperative computing, constellation cluster management and control and the like, and the research and development cost and complexity are reduced.

A computing services environment may include application gateways receiving application-layer request messages from various sources. The computing services environment may also include an autonomous agent platform configured to instantiate and execute an autonomous agent to evaluate network traffic associated with a portion of the computing services environment. The computing services environment may also include an orchestration engine configured to determine one or more mitigation policies corresponding with one or more of the application gateways based on identification of the application-layer distributed denial of service attack by the autonomous agent. The computing services environment may also include application-layer web application firewalls corresponding to the plurality of application gateways and implementing the one or more mitigation policies to prevent a subset of subsequent application-layer request messages from the subset of the sources from reaching one or more components of the computing services environment.

The invention belongs to the field of artificial intelligence, particularly relates to an integrated artificial intelligence analysis and modeling platform supporting large-scale data processing, and aims to solve the problems of low resource scheduling efficiency, model training reasoning splitting, data algorithm coupling, limited cross-modal modeling and the like under high-dimensional heterogeneous mass data. The platform comprises a distributed data access layer, a unified metadata governance engine, a heterogeneous computing resource scheduling center, a model life cycle management module, a multi-modal fusion modeling framework and a dynamic service orchestration interface, and realizes automatic data access governance, dynamic resource scheduling, model full-cycle management and control and cross-modal intelligent fusion. And the resource utilization rate, the modeling accuracy and the service deployment efficiency are remarkably improved.

A computing services environment may include application servers providing computing services including access to a database system, a unified metadata framework including autonomous agent definitions referencing action definitions defining a plurality of actions capable of being performed within the computing services environment, an agent service configured to instantiate an autonomous agent instance based on an autonomous agent definition, and an orchestration layer configured to determine an orchestration plan based on novel planning text generated by a generative language model. The orchestration plan may include a subset of the plurality of actions identified in the novel planning text. The computing services environment may execute the subset of the plurality of actions within the computing services environment.

A computing services environment may include application gateways receiving application-layer request messages from a plurality of sources. The computing services environment may also include an orchestration engine configured to identify an application-layer distributed denial of service attack based on input data characterizing network traffic received at the application gateways and to determine a mitigation plan update to address the application-layer distributed denial of service attack. The computing services environment may also include an autonomous AI agent platform configured to instantiate and execute an autonomous AI agent instance configured to determine whether to approve or reject the mitigation plan update by evaluating the mitigation plan update via a generative language model. The computing services environment may also include application-layer web application firewalls corresponding to application gateways. The orchestration engine may instruct the application-layer web application firewalls to implement the mitigation plan update upon approval by the autonomous AI agent instance.

The invention belongs to the technical field of artificial intelligence middleware and cloud computing task arrangement, and discloses an agent arrangement system and method based on a graph structure and a response type programming technology. Comprising a graph structure construction and compiling unit, a node operation unit, a routing and confluence control unit, a graph signal and event management unit and a response type operation and execution regulation and control unit, and an execution path has integrity and verifiability before deployment through analysis of a service graph, topology verification and confluence supplementation of parallel branches; the nodes trigger execution according to graph signals and write products into a result data area or a shared data area, and the routing and confluence control determines a convergence mode of signal transmission and parallel branches according to a connection predicate and a confluence rule. The event management unit generates a structured event for a node operation state, and the operation regulation and control unit schedules through a leading edge queue and in-transit counting and feeds back a back pressure signal to the upstream when a load changes, so that stable propulsion of a graph structure is realized.

A computing services environment may include application servers providing computing services including access to a database system, a unified metadata framework including autonomous agent definitions referencing action definitions defining a plurality of actions capable of being performed within the computing services environment, an agent service configured to instantiate an autonomous agent instance based on an autonomous agent definition, and an orchestration layer configured to determine an orchestration plan based on novel planning text generated by a generative language model. The orchestration plan may include a subset of the plurality of actions identified in the novel planning text. The computing services environment may execute the subset of the plurality of actions within the computing services environment.

An adaptive orchestration platform routes quantum-computing jobs to optimal backends in real time. A broker service ingests a quantum job description expressed in a provider-agnostic canonical schema, polls status metrics for multiple candidate processors, and derives composite scores using machine-learned, user-weighted factors. The highest-scoring backend is selected, and the job is automatically translated into its native instruction format. Payment is secured by a blockchain escrow smart contract that releases segmented disbursements upon queue confirmation, execution start and verified completion against service-level parameters. Results are hashed to an immutable ledger and failures trigger automated re-routing or refunds.

A method for database constraint generation, executed by at least one processor on a computing device accessing one or more large language models (LLMs), comprising retrieving data and / or metadata from a database; generating prompts by parameterizing inputs with concrete values; interacting with LLMs through these prompts to obtain and analyze responses; and performing data intelligence processing to derive natural-language descriptions of structural database elements. The method enables generating database constraints from defined classes, such as attribute-domain restrictions, intra-relational, and inter-relational constraints. Constraints include semantic, syntactic, and dependency-based types. Orchestration of constraint learning involves predefined or dynamic workflows incorporating tasks like database sampling, constraint testing, and refinement. It employs LLM-based techniques to generate candidate rules and optimize constraints through iterative testing and scoring. The method further supports counterexample identification, score aggregation, and rule evaluation to ensure robust constraint generation and refinement.

The application discloses a cloud measurement and control arrangement architecture, method and system based on micro services, belongs to the field of combination of spaceflight measurement and control and cloud computing, and comprises: a service arrangement architecture after cloudization of a measurement and control baseband, which adopts micro services and a layered thought, performs hierarchical and service division of the arrangement architecture, and realizes high availability, high cohesion, low coupling and scalability of the cloud baseband arrangement architecture; a customizable arrangement rule model, which realizes self-arrangement and scalability of rules, and improves richness of the micro service arrangement architecture; and open access capability of a scheduling algorithm service, which can randomly access diversified scheduling services online, forms a dynamically scalable online algorithm library, provides flexible, balanced and optimized deployment for measurement and control application arrangement, and enhances scheduling capability of the micro service arrangement architecture. The application can comprehensively realize service, diversification and open arrangement of measurement and control resources.

Systems and methods for managing communications between provider-specific computing resources and a central management platform are provided. A persistent connection is established between a management worker and the central management platform, which may be deployed in a first network. The management worker receives connection requests from agents of provider-specific computing resources, which may be deployed in a second network. A multiplexed message stream is established using the persistent connection in response to the connection requests. The management worker relays messages between the agents and the central management platform through the multiplexed message stream, enabling efficient communication across different networks.

The invention provides a large model management software and hardware integrated device and a management method. The large model management software and hardware integrated device comprises a heterogeneous computing unit, a computing power processor integrating multiple architectures and a storage device. The framework scheduling unit is deployed on the heterogeneous computing unit, dynamically allocates computing power based on physical resources of the heterogeneous computing unit and adapts to an AI framework; the model full-period management unit is used for directly calling the execution capability of the framework scheduling unit and completing model training, parameter storage and reasoning service packaging; the application construction unit is connected with the reasoning service of the model full-period management unit through a standardized interface and provides knowledge enhancement, agent cooperation and application arrangement capabilities; and the optimization decision-making unit is used for collecting operation data of the heterogeneous calculation unit and the model full-period management unit in real time, dynamically generating a resource configuration strategy and feeding back the resource configuration strategy to the framework scheduling unit to form closed-loop control.

The application discloses a container virtual machine resource management method and device and electronic equipment, and belongs to the technical field of computers. The method comprises the following steps: deploying a container orchestration component on a computing power management platform; adding a control plane cluster and N computing power member clusters based on the container orchestration component, wherein the computing power member clusters are used for deploying container virtual machines, N is an integer greater than 1; configuring the scheduling weight of the corresponding container virtual machine for each computing power member cluster; in the case that the computing power management platform is in a running state, obtaining the computing power resources and the number of container virtual machines of each computing power member cluster; based on the computing power resources and the number of container virtual machines of each computing power member cluster, a corresponding scheduling strategy is generated, and the control plane cluster is used for executing the scheduling strategy.

The invention discloses a server-free edge cloud collaborative arrangement system and method based on hierarchical reinforcement learning, and belongs to the technical field of server-free computing. The system comprises a three-layer control module and a socialized cross-layer coordination module, wherein a level I ready state manager adopts an A2C algorithm and outputs an enabled ready state set; the hierarchy II deployment scheduler adopts a GCN-A2C algorithm to determine the placement and activation of the working nodes in the edge server, and outputs the ready state of the working nodes and the activation of new nodes; the hierarchy III calls the gateway cluster to adopt an MAA2C algorithm, and outputs request-working node mapping according to the request and the edge resource state; and the socialized cross-layer coordination module realizes time synchronization, information intercommunication and resource matching of three layers of decisions through a time alignment unit, a decision interaction unit and a resource coupling unit. According to the system, the problems of state explosion, difficulty in cross-layer collaboration and the like of server-free edge cloud arrangement are solved, the service quality is improved, the cost is reduced, the load is balanced, and the overall performance of the system is optimized.

System for the orchestrated management of identity security functions in container-based multi-cloud environments, comprehensive an identity data capture unit, trained to capture identity-related security parameters from multiple distributed, container-based computing environments, an orchestration control unit, trained to coordinate the control of identity and access security functions across multiple cloud environments, a policy linking unit, trained to assign safety rules to container-related execution states, and a security enforcement unit trained to execute identity-related security measures within the respective computing environment, the system coordinates identity security functions across environments without imposing a fixed prioritization of individual cloud environments.

This invention belongs to the technical field of server resource scheduling and management, and relates to a cascaded scheduling system for server AI computing power modules for elastic computing. This system utilizes a situational awareness unit deployment module to collect micro-level operational data from the modules and generate raw sampling information; a real-time situational vector generation module constructs a multi-dimensional real-time situational vector; a global pressure map construction module maps the vector to generate a global cascaded pressure map for the pipeline; a dynamic bottleneck diagnosis module identifies abnormal nodes and determines hard bottlenecks and pseudo-blockages; a collaborative flow control strategy orchestration module generates push-pull linkage control commands; a predictive performance state adjustment module generates power level adjustment commands based on prediction time; and a command issuance and feedback closed-loop module drives execution and corrects parameters. This invention solves the problems of difficult accurate diagnosis of micro-level performance bottlenecks and the lack of global collaborative dynamic adjustment capabilities for data flow and power consumption states in heterogeneous AI computing power module cascaded systems.

This invention discloses a method and system for unified orchestration and seamless migration of containers for heterogeneous computing power pools. The method includes establishing hierarchical state records corresponding to each inference layer of a large model; collecting resource pressure indicators of the source device and triggering a pre-replication process before the source device becomes resource saturated; responding to the triggering of the pre-replication process, serializing the running state of each inference layer of the large model into an architecture-independent intermediate representation based on the hierarchical state records and transmitting it to the target device, while recording the inference layers that are written and updated during the transmission to form a dirty layer list; on the target device, deserializing all received architecture-independent intermediate representations into the target device's native running state format, reconstructing the inference execution state of the large model's inference container and resuming operation until migration confirmation is complete. This invention compresses service interruption time to an extremely brief suspension phase that only requires retransmission of the dirty layer list, achieving near-seamless migration of inference services.

Systems and methods are provided for use in orchestrating data connections. One example computer-implemented method includes receiving, by an orchestration host computing device, an access request for a user and, in response to the request, identifying, through an open service, multiple accounts issued to the user by different account hosts. The computer-implemented method also includes identifying, by the computing device, consent terms for each of the account hosts, aggregating the identified consent terms for the account hosts, and presenting, at a communication device associated with the user, the aggregate consent terms to the user. The computer-implemented method then includes, based on acceptance from the user of the aggregate consent terms, submitting, by the computing device, a data connection request to each of the account hosts.