95 results about "Distributed Computing Environment" patented technology

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.

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.

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.

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.

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.

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.

Embodiments of the present disclosure relate to the automated optimization and / or evaluation of parameterizable models. In some embodiments, data is recorded or acquired during the optimization of the parameterizable model according to a user instruction. Based on such recording or acquisition, a parameter is then updated during the optimization in some embodiments, for example, through local and / or global optimization. In some embodiments, validation and / or benchmarking is performed based on a type of parameterizable model and one or more performance metrics. In some embodiments, local and / or global validation is performed as part of the validation and / or benchmarking.

Method and apparatus are provided for configuring VXLAN in a distributed computing environment. The method comprises receiving, by a management agent of each node in a cluster of nodes, a configuration specification from a central management system; at each node, discovering, by the management agent of each node in the cluster of nodes, broadcast information of peer nodes in the cluster of nodes; creating, by the management agent of each node in the cluster of nodes, a state model database of the peer nodes based on the broadcast information of the peer nodes and the configuration specification; establishing a VXLAN using the state model database of the peer nodes; and updating the VXLAN dynamically in response to changes in the cluster of nodes using the state model database of the peer nodes.

The invention discloses a time sequence perception learning adaptive load balancing method for a distributed computing environment, and relates to the field of distributed computing. The problems that a load balancing method in a distributed computing environment mostly depends on a static rule or only performs scheduling based on an instantaneous system state, load change time correlation is difficult to fully utilize, response to dynamic load change is lagged, and adaptive capacity is insufficient are solved. The method comprises the following steps: performing arrangement, modeling and time sequence prediction on historical load monitoring data of a computing node, and constructing a time sequence prediction model; the load balancing method comprises the following steps: calculating a load-weight mapping process, predicting historical load monitoring data of a node, collecting the historical load monitoring data of the node, calculating a load trend factor reflecting a future load change direction and change intensity, and introducing the load trend factor into the load-weight mapping process to generate a dynamic load balancing weight constrained by a trend; and training, evaluating, predicting and executing the load balancing strategy through the deep reinforcement learning model.

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.

The invention discloses automated verification and benchmarking of parameterizable models in a distributed computing environment. 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.

Systems and methods for dynamically modifying application conditions are disclosed. A system can provide a respective interface for a network-accessible application to a plurality of client devices. The system can randomly select an initial subset of the plurality of tiles for display at the client devices. The system can generate initial application state values based on the plurality of tiles less the initial subset. The system can provide the one or more initial application state values to the plurality of client devices for display. The system can receive input values from the client devices. The system can execute the network-accessible application according to the input values. The system can generate a respective output value for each client device based on the input values.

System for low-latency identity verification and cryptographic key management in distributed computing environments, comprehensive a local processing unit trained to perform identity-related checks within the respective computing environment, a cryptographic key management unit, designed for the secure storage and use of cryptographic keys within the computing environment, and An authentication control unit trained to coordinate multi-stage authentication processes using cryptographic keys, whereby identity verification and cryptographic key usage take place locally within the respective computing environment, without dependence on external central security services.

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.

Method and apparatus are provided for managing IP address in a distributed computing environment. The method comprises gathering, by a management agent (MA) of each node in the cluster of nodes, broadcast information of peer nodes; identifying, by the MA of each node in the cluster of nodes, one or more nodes without an overlay IP address using the broadcast information gathered about its peer nodes; electing a candidate node among the one or more nodes without an overlay IP address using the broadcast information gathered about its peer nodes; assigning, by the MA of the candidate node, a next available overlay IP address to the candidate node; and communicating, among the cluster of nodes, in accordance with the assigned overlay IP address of the candidate node.

Certain embodiments of the present disclosure provide techniques for allocating resources associated with tokens on a blockchain across resource providers in a distributed computing environment. An example method generally includes partitioning a total resource pool into a plurality of resource pools. Generally, each respective resource pool of the plurality of resource pools is associated with a respective resource provider. For each respective resource pool of the plurality of resource pools, the respective resource pool is partitioned into a reserve pool and an active pool based on an on-blockchain transaction history associated with tokens backed by the total resource pool. Transactions associated with the tokens are processed using one or more resource pools from the plurality of resource pools.

A system and methods for multi-type data compression or decompression with a virtual management layer in a distributed computing environment, comprising. It incorporates a virtual management layer to organize incoming data types and allocate compression or decompression tasks across multiple computing devices, selecting techniques best suited for particular data types. Associated data sets may be flagged prior to processing, ensuring preservation of relationships even when compressed or decompressed on different devices. This distributed approach allows efficient parallel processing of multiple data types, improving scalability and performance. A load balancing module optimizes task distribution based on available resources and processing requirements. The system enables each data type to be processed using the most efficient technique while maintaining associations between related data sets, effectively handling larger volumes of diverse data.

Systems and methods for preprocessing large inference files in a cluster environment prior to transmission to one or more downstream applications. The inference files are processed using templates that correspond to particular downstream applications, allowing for optimized transmission and optimized processing by each downstream application.

The disclosed embodiments include computer-implemented processes and systems that establish configurable pipelines for training and deploying machine-learning processes in distributed computing environments. For example, an apparatus may obtain elements of configuration data associated with a plurality of application engines from the memory and may execute sequentially each of a subset of the application engines in accordance with a corresponding one of the elements of configuration data. The executed subset of the application engines may perform operations that at least one of (i) train a machine-learning or artificial-intelligence process or (ii) apply the trained machine-learning or artificial-intelligence process to an input dataset. The apparatus may also transmit artifact data generated by at least one of the executed subset of the application engines to a computing system.

Techniques are disclosed for dynamically rebalancing a caching layer within a distributed database system hosted across a distributed computing environment. In some embodiments, a system that includes a plurality of physical nodes implementing a hosting service deploys containers that serve as caching nodes for the distributed database system. Each container is configured to store cached data within a memory internal to its respective physical node. The system monitors the storage utilization and read / write activity across the caching containers, and based on this monitoring, redistributes data to balance the load across the cluster. Rebalancing can include identifying underutilized and overutilized containers, retrieving subsets of data from overworked containers from persistent storage, and storing the data in underutilized containers.

Systems and methods for training a language model using historical data structures are disclosed. A system can maintain, in one or more data structures, data corresponding to a plurality of data structures. The system can generate, using the data corresponding to the plurality of historical data structures, a training dataset comprising a plurality of training examples. At least one training example can include a respective input prompt indicating an intent relating to data structures and a respective output message identifying information associated with at least one historical data structure of the plurality of historical data structures. The system can update a language model using the training dataset.

The present specification relates to systems and methods for processing transaction messages in distributed computing environments. The disclosed system includes an orchestration engine that interacts with a plurality of computing engines and micro-services to manage different aspects of transaction messages. The orchestration engine receives transaction data, identifies relevant services, determines their availability, and coordinates their execution to complete processing tasks. The system's architecture allows for dynamic adjustment of service prioritization and resource allocation based on real-time conditions, enhancing the efficiency of handling transaction messages. Additionally, caching mechanisms store service availability information to reduce processing overhead in subsequent transactions. The described methods facilitate improved coordination among computing engines, supporting scalability and adaptability in diverse transaction processing scenarios.