240 results about "Distributed Computing Environment" 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.

In some examples, computer-implemented systems and processes deploy securely de-risked elements of confidential data within a distributed computing environment. For example, an apparatus may obtain configuration data associated with a source data table. The configuration data may specify an identifier of a column of the source data table that includes elements of confidential data, and based on the configuration data, the apparatus perform operations that anonymize the elements of confidential data within the column of the source data table and generate an anonymized column within the source data table. The apparatus may also perform operations that provision an anonymized data table that includes the anonymized column to at least one computing system, which may process the anonymized data table and generate an output data table that includes the anonymized column and maintains a referential integrity between the source data table and the output data table.

In some examples, computer-implemented systems and processes deploy securely de-risked elements of confidential data within a distributed computing environment. For example, an apparatus may obtain configuration data associated with a source data table. The configuration data may specify an identifier of a column of the source data table that includes elements of confidential data, and based on the configuration data, the apparatus perform operations that anonymize the elements of confidential data within the column of the source data table and generate an anonymized column within the source data table. The apparatus may also perform operations that provision an anonymized data table that includes the anonymized column to at least one computing system, which may process the anonymized data table and generate an output data table that includes the anonymized column and maintains a referential integrity between the source data table and the output data table.

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.

The invention discloses a cross-domain computing task processing method, a program product, equipment and a medium, and relates to the technical field of cloud computing. The method comprises the following steps: determining a computing node for executing a cross-domain computing task in a cross-domain collaborative scene, and generating an identity certificate bound with the computing node based on a hardware credible state of the node; collecting security policies of a plurality of management domains for conflict detection and decision, and generating a conflict-free session policy; scheduling the cross-domain computing task to a target computing node meeting a preset credible requirement, and issuing a revocable dynamic session token; and recording the operation information of the cross-domain calculation task in the whole life cycle as an audit event, and generating a chained audit log which is linked by the hash value and is digitally signed through a preset verifiable audit interface. By means of the technical scheme, it can be ensured that the whole life cycle of any computing task meets the closed-loop safety requirements of identity credibility, permission controllability, execution propriability and behavior traceability in the complex distributed computing environment.

Disclosed herein are systems and methods for enabling decentralized, schema-driven interoperability across distributed computing environments through the use of a Standard Knowledge Language (SKL). An Enterprise Mesh Platform (EMP) interprets and executes SKL specifications—such as capabilities, objects, mappings, policies, and workflows—as composable, machine-interpretable contracts that define data structures, logic, and governance protocols. The system supports dynamic versioning, validation, semantic linking, and recursive execution of SKL-defined components. A mesh-wide analytics server coordinates execution, issue detection, and resolution propagation. Capabilities can be orchestrated, remediated, and adapted in real-time based on SKL-defined relationships, while preserving compliance and traceability. The disclosed architecture facilitates federated development, adaptive system integration, and fine-grained policy enforcement across complex digital ecosystems.

The invention provides an intelligent computing power scheduling method in a distributed computing environment, and relates to the technical field of distributed computing, and the intelligent scheduling method specifically comprises the steps of collecting resource information data, constructing a resource and task model, evaluating a computing power demand, formulating an adjustment strategy, distributing and scheduling the computing power, monitoring and adjusting in real time, and feeding back and optimizing. Through comprehensive modeling and dynamic computing power evaluation of computing nodes and tasks and in combination with multiple intelligent scheduling strategies, accurate allocation of computing power resources can be realized, the problems of resource waste and node load imbalance are effectively avoided, the overall utilization rate of resources in a distributed computing environment is remarkably improved, and the computing power of the distributed computing environment is improved according to the characteristics and requirements of the tasks. The execution nodes are reasonably selected, the resource allocation is optimized, the task correlation is considered, the data transmission overhead is reduced, the task execution speed can be increased, the task completion time can be shortened, and the processing capacity and response speed of the system are improved.

The invention belongs to the technical field of computing power scheduling, and particularly relates to a computing power scheduling method in a distributed computing environment. According to the task scheduling method and system, the task needing to be processed at present is obtained through the task management system, the task is divided into the real-time task set, the priority task set and the common task set, and the related tasks are scheduled by adopting the corresponding scheduling strategy based on the set corresponding to the current task and the priority in the set. Resource scheduling and allocation in a distributed computing environment are realized; according to the method, through the steps of task classification, real-time resource monitoring, resource demand estimation, priority ranking, reasonable scheduling strategy, load calculation, resource migration and the like, the technical problem of computing resource scheduling and allocation in a distributed environment is effectively solved, timely processing of high priority is ensured, the system load is balanced, and the system performance is improved. And efficient utilization of resources and stable operation of the system are realized, so that the performance and reliability of the whole distributed computing environment are improved.

The invention provides a distributed data calculation engine scheduling and resource dynamic optimization method and system, and relates to the technical field of distributed data, and the method comprises the steps: receiving a distributed calculation task, determining a resource demand parameter, constructing a multi-level state space based on historical execution data, and carrying out the iterative optimization of a dynamic reward mechanism, and selecting a target node group and constructing a network topology structure, analyzing and generating a data transmission path and a bandwidth allocation scheme through a graph neural network, executing task allocation and carrying out resource load monitoring. According to the invention, the utilization rate of computing resources is improved, the data transmission delay is reduced, and intelligent resource scheduling and optimization in a distributed computing environment are realized.

Systems and methods for synchronizing data structures in computer networks and distributed computing environments are disclosed. A system can provide a network resource to a client device corresponding to a network profile, establish a network communication session with the client device, and transmit instructions for network application formatting during the network communication session. The system can receive a second request for a network exchange during the network communication session. The system can determine that a path for an object of the network communication session satisfies at least one intersection criterion, the at least one intersection criterion associated with at least one second transmission value. The system can update a second data structure of the network profile.

A data pipeline validation system and method configured to partially automate testing of data pipelines in a distributed computing environment. The system includes a data pipeline analytic device equipped with various modules, such as a query generation module, data frame comparison module, and metadata management module. The query generation module employs natural language processing techniques to analyze configuration entries and dynamically generate SQL queries tailored to specific test cases. The data frame comparison module compares the results of different test cases using distributed collections, enabling parallel processing and efficient result comparison. The metadata management module captures and stores relevant metadata for traceability and auditing purposes. The system facilitates comprehensive validation of data pipelines, enabling organizations to ensure the accuracy, reliability, and integrity of data.

Embodiments of the present disclosure relate to automated optimization and / or evaluation of parameterizable models. With respect to optimization, some embodiments record or collect data according to a user instruction during the optimization of the parameterizable model. Based on such recording or collection, some embodiments then update a parameter during optimization, such as via local and / or global optimization. With respect to evaluation, some embodiments perform validation and / or benchmarking based on a type of parameterizable model and one or more performance metrics. Some embodiments perform local validation and / or global validation as part of the validation and / or benchmarking.

Various methods and processing systems for the effective management of network traffic and facilitating data forwarding within distributed computing environments. Network components may be configured to amalgamate the Border Gateway Protocol (BGP) with Source Network Address Translation (SNAT) or network address port translation (NAPT) technologies to facilitate dynamic notification of network address accessibility and use port index identifiers to augment the precision and resilience of routing. A distributed SNAT / NAPT virtual network function (VNF) or cloud-native network function (CNF) may collaborate with LEAF switches or server aggregation devices to refine data transmission via adaptable address mapping and forwarding and enhance network scalability and resilience.

Systems and methods are disclosed for anomaly detection using a “detect and collect” cybersecurity monitoring approach. Initially, a cybersecurity monitoring system obtains and analyzes a baseline subset of telemetry data from computing resources to detect potential anomalies indicative of cybersecurity threats. Responsive to identifying such anomalies, the system selectively determines additional, contextually relevant telemetry data for targeted collection. This selective data collection significantly reduces telemetry volumes, enhancing efficiency and scalability. An intelligent data fabric and dynamic security knowledge graph are employed to enrich telemetry data in real-time, enabling comprehensive anomaly characterization, risk scoring, and automated security responses. The disclosed techniques support multimodal and multiresolution anomaly detection, adaptive learning, and rapid threat response within diverse distributed computing environments.

Disclosed herein are systems and methods for enabling decentralized, schema-driven interoperability across distributed computing environments through the use of a Standard Knowledge Language (SKL). An Enterprise Mesh Platform (EMP) interprets and executes SKL specifications — such as capabilities, objects, mappings, policies, and workflows — as composable, machine-interpretable contracts that define data structures, logic, and governance protocols. The system supports dynamic versioning, validation, semantic linking, and recursive execution of SKL-defined components. A mesh-wide analytics server coordinates execution, issue detection, and resolution propagation. Capabilities can be orchestrated, remediated, and adapted in real-time based on SKL-defined relationships, while preserving compliance and traceability. The disclosed architecture facilitates federated development, adaptive system integration, and fine-grained policy enforcement across complex digital ecosystems.

According to the water conservancy multi-model coupling parallel architecture design method, the whole water conservancy system is divided into a plurality of basic forecast sections, and the forecast sections are mapped according to the upstream and downstream topological relation of the forecast sections. Meanwhile, a plurality of calculation units are divided by utilizing drainage basin service characteristics and different models, and each unit corresponds to one calculation node except for mapping with a basic forecast section; the computing nodes are mutually connected through a high-speed network to form a distributed computing environment. Corresponding water conservancy model components such as a hydrological model, an engineering scheduling model, a hydrodynamic model and an incoming water forecasting model are deployed on each computing node, and the model components on different nodes can be combined and cooperatively work according to needs so as to realize comprehensive simulation of the whole water conservancy system.

A computer implemented method of deployment and resource allocation of microservices of a distributed computing environment is disclosed. The distributed computing environment includes a microservice deployment scheduler and one or more computing nodes. The microservice deployment scheduler includes a reinforcement learning based dynamic workload orchestration module. The method includes receiving microservice constraints descriptive of a microservice computing task by the microservice deployment scheduler. The method further includes receiving node specific properties from the one or more computing nodes by the microservice deployment scheduler. The node specific properties are descriptive of a computing capacity and / or computing capabilities of the one or more computing nodes. The method further includes orchestrating operation of the one or more computing nodes by the microservice deployment scheduler by inputting the microservice constraints and the node specific properties into the reinforcement learning based dynamic workload orchestration module.

Disclosed herein is a computer implemented method of deployment and resource allocation of microservices of a distributed computing environment. The distributed computing environment comprises a microservice deployment scheduler and one or more computing nodes. The microservice deployment scheduler comprises a reinforcement learning based dynamic workload orchestration module. The method comprises receiving microservice constraints descriptive of a microservice computing task by the microservice deployment scheduler. The method further comprises receiving node specific properties from the one or more computing nodes by the microservice deployment scheduler. The node specific properties are descriptive of a computing capacity and / or computing capabilities of the one or more computing nodes. The method further comprises orchestrating operation of the one or more computing nodes by the microservice deployment scheduler by inputting the microservice constraints and the node specific properties into the reinforcement learning based dynamic workload orchestration module.

An illustrative method includes a global control plane managing a plurality of local control planes that manage a respective plurality of distributed computing environments, each of the plurality of local control planes configured to manage its respective computing environment in accordance with a mode of operation that is selected from a plurality of modes of operation based on a respective connectivity status with the global control plane, wherein managing the plurality of local control planes comprises selectively deploying an update to one or more local control planes of the plurality of local control planes based on the connectivity statuses of the plurality of local control planes with the global control plane. In some embodiments, the global control plane receives log data associated with the update and deploys, based on the log data, a second update to the one or more local control planes.

A storage management system that provides persistent storage to containerized applications running in a cluster of nodes may be configured to identify an anomalous pattern of storage operations associated with a container instance deployed in the cluster of nodes, determine, based on the anomalous pattern of storage operations, a potential security threat associated with the container instance, and perform a remedial action based on the potential security threat.

Systems and methods for synchronizing data structures in computer networks and distributed computing environments are disclosed. A system can provide a network resource to a client device corresponding to a network profile, establish a network communication session with the client device, and transmit instructions for network application formatting during the network communication session. The system can receive a second request for a network exchange during the network communication session. The system can determine that a path for an object of the network communication session satisfies at least one intersection criterion, the at least one intersection criterion associated with at least one second transmission value. The system can update a second data structure of the network profile.

Systems and methods are disclosed for generating a distributed execution model with untrusted commands. The system can receive a query, and process the query to identify the untrusted commands. The system can use data associated with the untrusted command to identify one or more files associated with the untrusted command. Based on the files, the system can generate a data structure and include one or more identifiers associated with the data structure in the distributed execution model. The system can distribute the distributed execution model to one or more nodes in a distributed computing environment for execution.

Asset error remediation is provided. Risk and classification of an asset error are analyzed to prioritize asset error remediation for an asset based on risk criticality, risk context, and vulnerability level corresponding to the asset by detecting suspicious behavior and risk exposure to the asset in a heterogeneous distributed computing environment using artificial intelligence. A priority of the asset error remediation is determined to fix the asset within the heterogeneous distributed computing environment based on the risk and the classification of the asset error. A set of action steps is performed to fix the asset within the heterogeneous distributed computing environment based on the priority of the asset error remediation.

The systems and methods taught herein are generally directed to facilitating a performing arts contest in a distributed computing environment having remote processing devices linked through a communications network in a streaming audio / video setting, including a dynamic point referencing of an audiovisual performance for an accurate and precise selection and controlled cycling of portions of the performance. The dynamic point referencing can be used by a learning artist, for example, in analyzing or performing a portion of the work through an accurate and precise digital audio / video instructional method having the controlled cycling feature. Such systems and methods will be appreciated, for example, by musicians, dancers, and other enthusiasts of the performing arts.

The described technology pertains to a distributed computing environment, specifically implementing a streaming protocol on a scalable object storage service. The technical problem addressed is the high computing resource demand of distributed streaming platforms. The solution involves separating content from location metadata, allowing centralized, scalable storage of metadata. Brokers store content in object storage services based on specific metrics, such as fast read / write times or greater storage capacity. A background platform remaps metadata sequences based on logical timestamps, optimizing data retrieval. This system enhances the efficiency of computing resources by reducing the load on brokers and improving data access speeds. A use can be in cloud computing systems for efficient content streaming and storage management.

Devices, methods, and systems for shutdown and restart management in a distributed computing environment are described herein. One method includes determining whether a backup of a workload in a distributed computing environment exists, causing a shutdown sequence of the workload to occur at a first predetermined time to shut down the workload, and causing a restart sequence of the workload to occur to restart the workload in the distributed computing environment.

A system for protecting sensitive data through vaulted and vaultless tokenization across a distributed computing environment is provided. The system includes a processor and memory containing multiple integrated modules to secure data transmission and storage. A tokenization application programming interface (API) receives sensitive data from the distributed computing environment through a network. An encryption module applies format-preserving cryptographic transformation to the sensitive data, creating encrypted data while preserving original data format and length characteristics. A tokenization engine receives the encrypted data and generates format-preserving tokens that include structural characteristics based on the sensitive data. A management module processes encrypted data and format-preserving tokens to generate cryptographic responses. An enforcement module applies authorization rules for access control based on user roles and data classification levels. The tokenization API provides the format-preserving tokens and the cryptographic responses back to the distributed computing environments according to established user roles and data classification.

The invention discloses a big data analysis method based on multi-modal data fusion. The method comprises the steps that a heterogeneous data source is obtained to generate a feature extraction module, a data fusion model is constructed and trained, and feature integration is achieved through a high-dimensional feature extraction unit and a low-dimensional feature extraction unit in combination with a dynamic weight distribution mechanism. The invention further relates to multi-modal data processing in the distributed computing environment, the master node and the slave nodes work cooperatively, and task scheduling and resource allocation are optimized. According to the method, the calculation complexity can be reduced, the analysis efficiency and the system expansibility can be improved, and an efficient solution is provided for multi-modal big data analysis.

The invention relates to the technical field of cloud computing, discloses a data processing method and system based on cloud computing, and aims at the problems of multi-node data flow dynamic change and communication delay. An asynchronous updating mechanism and a delay node isolation technology are adopted to process communication delay exceeding nodes, parallel node screening and asynchronous task distribution are combined to optimize computing resource distribution, and finally, a delay compensation strategy is utilized to coordinate gradient updating time of each node, so that efficient synchronization of gradient data in a distributed computing environment is realized. According to the method, the problem of data inconsistency caused by communication delay in a distributed environment is effectively solved, the gradient synchronization precision and efficiency are improved, and reliable technical support is provided for training of a large-scale distributed machine learning model.

A modular compliance verification and audit recording system is disclosed. The system includes a plurality of hardware modules interconnected via a system communications bus, a memory storing rule sets, access profiles, and encrypted audit records, and communication circuitry configured to interface with one or more external electronic devices through a secure network gateway. The system receives and authenticates compliance data, applies stored rule sets to determine verification results, records verified events as immutable audit entries, and enforces data-minimization and redaction policies to generate privacy-protected audit information. The processed audit information is transmitted over the secure network gateway to authorized external systems. The system provides end-to-end verification, recording, and privacy preservation of compliance-related data across distributed computing environments.