159 results about "Distributed Computing Environment" patented technology

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.

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.

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.

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.

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.

Described herein are interactive systems and methods for training language models using input from distributed computing environments. The system can receive prompts for a language model. Each prompt can include at least one common term corresponding to an intent relating to wagers. The system can determine that the language model has not been updated using training examples that include the at least one common term corresponding to the intent. The system can generate, using the prompts and additional information corresponding to the at least one common term, a set of training examples. Each set of training examples can include a respective prompt having the at least one common term. The system can update the language model using the set of training examples.

Systems and methods for integrating generative artificial intelligence (AI) with real-time data streaming platforms in distributed computing environments are disclosed. A real-time streaming platform receives a natural language input from a client device, stores a corresponding text request in a topic, and generates a prompt using a processing engine. The prompt is provided to a generative AI system, which generates a structured query language (SQL) query. The SQL query is stored in the topic and executed on a cloud SQL database to obtain an SQL result. The SQL result is stored in the topic and a response based on the SQL result is transmitted to the client device. This approach leverages real-time data streaming, automated prompt generation, and AI-driven query construction to facilitate accurate and timely access to distributed data sources.

The disclosed exemplary embodiments include computer-implemented systems, apparatuses, and processes that perform homomorphic computations on encrypted third-party data within a distributed computing environment. For example, an apparatus receives a homomorphic public key and encrypted transaction data characterizing an exchange of data from a computing system, and encrypts modelling data associated with a first predictive model, such as a machine learning model or an artificial neural network model, using the homomorphic public key. The apparatus may perform homomorphic computations that apply the first predictive model to the encrypted transaction data in accordance with the encrypted first modelling data, and transmit an encrypted first output of the homomorphic computations to the computing system, which may decrypt the encrypted first output using a homomorphic private key and generate decrypted output data indicative of a predicted likelihood that the data exchange represents fraudulent activity.

The invention discloses a dynamic load balancing method based on node and task classification matching, which relates to the field of distributed computing and comprises the following steps of: dividing nodes in a distributed computing environment into a computing type, a communication type, a storage type and a universal type based on quantitative indexes, the tasks are divided into a calculation-intensive type, a communication-intensive type, a storage-intensive type and a universal type based on a demand proportion; calculating a basic adaptation degree of the task and the node, and adjusting the adaptation degree to obtain a final adaptation degree; determining the total weight of the nodes, and performing initial task allocation through a multi-target weighted scoring strategy in combination with a task load quantification result, execution time estimation and a final adaptation degree; and performing node load state monitoring based on the load balancing factor, and triggering task migration to realize dynamic load balancing. According to the method, the system resource utilization rate, the task execution efficiency and the service quality are remarkably improved through classification matching and dynamic load balancing of the nodes and the tasks.

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.

A policy-based approach to execution of commands in a distributed environment involves applying policies to determine permissions for executing commands. In some implementations, a user inputs a command at a web portal, causing a request to be sent to a computer system. The web portal also sends an indication of one or more machine components of a remote system to which the command is to be applied. After identifying a policy associated with the user, the computer system evaluates a rule in the policy to determine whether the user is permitted to execute the command with respect to the one or more machine components. The computer system routes the command to the remote system for execution based on determining that the rule is satisfied. This enables the command to be executed without providing the user with direct or unrestricted access to the remote system.

The techniques described herein reduce the noise when making a health determination for an entity executing within, or supported by, a distributed computing environment. The system is configured to receive health data corresponding to resources upon which the entity depends. The health data can include an indication of whether an individual resource is healthy or unhealthy (e.g., based on values collected for metrics). The system uses hysteresis to reduce the noise when making a health determination for the entity. That is, the system calculates a historic center value (e.g., a historic average value) and a spread value (e.g., standard deviation) for the history center value, and uses the spread value to establish different thresholds for health state transitions.

The invention provides a task scheduling method, system and device based on multi-constraint optimization and a storage medium. The method comprises the following steps: acquiring a cluster real-time resource state and historical resource use characteristics of a to-be-executed task; determining constraint information based on the cluster real-time resource state and task configuration metadata of the to-be-executed task; the constraint information comprises an available resource constraint, a task concurrency constraint and a variable domain constraint; substituting the available resource constraint, the task concurrency constraint and the variable domain constraint into a linear programming model to solve the model; and generating a task scheduling strategy according to a model solving result. According to the method, the resource utilization efficiency and the task execution performance in a large-scale distributed computing environment are effectively improved.

Systems and methods for executing commands and scripts across operating systems and gathering results in a unified manner are provided. A method includes receiving one or more commands from a predefined list of commands from one or more devices in a distributed computing environment; receiving a set of targets distributed in the distributed computing environment; translating the one or more commands to a set of target commands, wherein each target command is associated with at least one target of the set of targets; transmitting a request to execute a script associated with each of the target commands to each of the targets; executing the script on each of the targets thereby generating a plurality of results corresponding to each of the target commands and each of the targets; and storing the plurality of results in a format for display in a user interface.

Automated optimization relating to parameterizable models in a distributed computing environment is disclosed. Embodiments of the present disclosure relate to automated optimization and / or evaluation of parameterizable models. As to the optimization, some embodiments record or collect data according to user instructions during the optimization of the parameterizable model. Based on such recording or collection, some embodiments then update parameters during optimization, such as via local and / or global optimization. For evaluation, some embodiments perform verification and / or benchmarking based on a type of parameterizable model and one or more performance metrics. Some embodiments perform local verification and / or global verification as part of verification and / or benchmark testing.

The techniques describe effective detection of network connectivity issues for a cloud service operating in a distributed computing environment. To detect the network connectivity issues, a system first projects network traffic patterns at the tenant level (e.g., on a tenant-by-tenant basis) and compares a tenant's current network traffic to the projected network traffic pattern. If the comparison yields that the current network traffic for the tenant is closely following the projected network traffic pattern, the tenant is deemed healthy. However, if the comparison yields that the current network traffic for the tenant is not closely following the projected network traffic pattern, the tenant is deemed unhealthy. Once the system has made these binary health determinations for various tenants on a tenant-by-tenant basis, the system is configured to aggregate the unhealthy determinations across a group of tenants to determine whether the cloud service is experiencing network connectivity issues.

A method to deploy a plurality of event-driven application components of an event-driven application in a distributed computing environment is described. The method includes automatically analyzing application source code of the event-driven application, using one or more processors, to identify relationships between the plurality of event-driven application components. Thereafter, a set of rules are applied to, based on the automatic analysis, generate assignment data recording assignments of event-driven application components to a plurality of computational nodes in the distributed computing environment. The set of rules is also applied to determine component requirements for each of the plurality of event-driven application components required to support execution at an assigned computational node in the distributed computing environment.

In one example, a system can operate an event ingest valve to pause event streaming to an event processing application executing on a first node of a distributed computing environment. The system can then access state data, of the event processing application, stored in a local memory of the first node. The system can generate a snapshot of the state data of the event processing application. After generating the snapshot, the system can shut down the event processing application on the first node. The system can then provide the snapshot to a second node of the distributed computing environment, the second node being configured to start the event processing application using the state data. After the event processing application is started on the second node, the system can operate the event ingest valve to resume the event streaming to the event processing application executing on the second node.

Systems and methods for large file upload, configurable with user workflows are disclosed. An upload service can receive a data file including metadata associated with a user, then upload the data file to a first database within a distributed computing environment. The upload service may identify an entitlement associated with the user based on the metadata and upload the data file to a second database. The upload service may determine a notification protocol based in part on the entitlement associated with the user, then generate one or more notifications based on the notification protocol. Each action performed by the upload service may be recorded and used to determine an event history of the data file. The upload service can store the event history.

Real-time transmission of IoT telemetry data to a third-party and use of such data by the third-party for purposes of determining IoT device-related recommendations specific to the IoT device user. Machine learning models receive the IoT telemetry data, as a least a part of the inputs, to determine recommendations that are tied to the IoT device and specific to the IoT device user. Non Fungible Tokens (NFTs) are minted by the IoT device manufacturer / distributor and hold a link to an IoT device identifier and an IoT device user identifier and include a smart contract formed between the third-party and the IoT device user that defines the criteria for the telemetry data that is to be acquired by the third-party. Use of the NFT allows for IoT gateways / data listeners to determine which IoT telemetry data is to be transmitted to the third-party and allows for the transmission to occur without identifying the IoT user in the transmissions.

Systems and methods for large file upload, configurable with user workflows are disclosed. An upload service can receive a data file including metadata associated with a user, then upload the data file to a first database within a distributed computing environment. The upload service may identify an entitlement associated with the user based on the metadata and upload the data file to a second database. The upload service may determine a notification protocol based in part on the entitlement associated with the user, then generate one or more notifications based on the notification protocol. Each action performed by the upload service may be recorded and used to determine an event history of the data file. The upload service can store the event history.

The present disclosure relates to techniques for service orchestration within a distributed pod based system. Particularly, aspects are directed to receiving, at a kernel residing on a distributed computing environment, a request to initiate deployment for a subservice or service on the distributed computing environment. The subservice or service has a type, and in order for the subservice or service to be deployed on the distributed computing environment, the type of the subservice or service has to be one that the distributed computing environment is configured to support. In response to receiving the request and the type of the subservice or service being one that the distributed computing environment is configured to support, specified resources are provisioned, and the subservice or service is deployed using a replica of a pod containing the provisioned specified resources and a modified image for the type of the subservice or service.

Disclosed herein is an agent-first application architecture that facilitates intelligent, metadata-driven interactions across distributed computing environments. The system comprises a Metadata Repository, which defines communication policies, interaction rules, and contextual parameters. An Agent Orchestration Layer governs autonomous software agents, dynamically managing their lifecycle, task delegation, and execution logic. A Self-Learning Engine continuously refines agent decision-making models through real-time metadata updates and adaptive learning mechanisms. The system further incorporates an Event-Driven Middleware, which enables real-time, event-triggered execution of agent actions, ensuring adaptive, scalable, and autonomous decision-making across complex multi-system environments. By eliminating rigid API dependencies and leveraging metadata-centric intelligence, the architecture enhances cross-system interoperability, resilience, and adaptability. The invention is particularly suited for dynamic, multi-agent ecosystems requiring real-time adaptability, self-optimization, and intelligent orchestration of system interactions.