1641 results about "Cloud resources" patented technology

The invention provides a digital power grid power cloud resource real-time hierarchical distribution method, which comprises the steps of dividing power cloud resources into a plurality of sub-resource pools according to a physical architecture and a topological structure of a digital power grid, setting a special resource pool for key equipment and sensitive loads of the power grid, and dynamically adjusting network topology and a routing strategy. A low-delay and high-reliability network transmission channel is provided through a priority queue and a bandwidth guarantee mechanism; for each sub-resource pool, extracting periodicity, tendency and randomness characteristics of a load by analyzing historical load data and a current system state of power grid equipment, and updating a load prediction result in real time; after incremental resources are migrated, a power grid control request is routed to a corresponding computing node by adopting a consistent Hash algorithm in a sub-resource pool, load balancing is realized by dynamically adjusting the number and distribution of virtual nodes of a Hash ring, and weights of the virtual nodes are dynamically adjusted according to load optimization indexes of the nodes so as to adapt to power grid load changes.

The invention discloses a risk control credit monitoring method based on cloud computing, and belongs to the technical field of cloud computing, and the risk control credit monitoring method based on cloud computing comprises the following steps: S1, collecting user transaction data and behavior track data in real time; s2, cleaning and standardizing the data; s3, constructing a multi-dimensional risk assessment model based on machine learning; s4, dynamically generating a credit score according to the risk characteristics; s5, triggering an early warning mechanism for abnormal transactions in real time; and S6, generating a visual risk control report and updating a monitoring strategy. According to the method, multi-source heterogeneous data are integrated through federated learning, hierarchical privacy protection is realized in combination with homomorphic encryption and differential privacy, risk assessment real-time performance is improved by using a hybrid cloud resource scheduling and dynamic model updating technology, and a compliance audit closed loop is constructed based on a block chain and interpretability analysis.

The present invention relates to a deep reinforcement learning-based cloud data center adaptive efficient resource allocation method. First, an operation (a scheduling job) is selected according to a score evaluated by critic (an evaluation operation) by using an actor parameterized policy (resource allocation). Then, a resource allocation policy is updated through gradient boosting, and a variance of a policy gradient is reduced by using an advantage function, to improve training efficiency; and wide simulation experiments are performed by using real data from a Google cloud data center. Compared with two advanced DRL-based cloud resource allocation methods and five classic cloud resource allocation methods, the method provided in the present invention has higher quality of service (QoS) in terms of a delay and a job dropout rate and has higher energy efficiency.

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

The present application discloses a cloud resource prediction method and system based on a GC-LSTM neural network model, an electronic device, and a readable storage medium. The method comprises two stages: a training stage and a prediction stage. In the training stage, historical data of cloud resources is collected and normalized to obtain a training data set, input data and labels used for training a neural network are organized and made, and an established GC-LSTM neural network model is trained. In the prediction stage, by means of acquired real-time sampling data, the trained model is loaded to perform result prediction, and, on the basis of a prediction result, resources may be scheduled and distributed to respond in advance. The present application aims to predict customers' demands for resources in advance and optimize customers' cloud experience. The model provided by the present application is simple and has few parameters, can guarantee the real-time performance of the prediction result, is practical and feasible, is suitable for the prediction of cloud resource utilization, and provides a guarantee for instant scheduling of resources.

The invention relates to the technical field of cloud computing resource migration, in particular to a cross-cloud resource migration method and system, and the method comprises the following steps: carrying out the proportional distribution of the processing capability of a virtual machine instance based on the processing capability of the virtual machine instance, the block storage read-write rate and the network delay time, carrying out weighted average on the block storage read-write rate and the network delay time, integrating the block storage read-write rate and the network delay time into a two-dimensional matrix, sorting and removing low-threshold nodes, and generating a preliminary resource optimization data set; according to the method, resource optimization accuracy is improved through proportional allocation of virtual machine processing capacity, weighted average block storage read-write rate and network delay and performance index matrix processing, performance weight difference and path parameter optimization performance dependency expression are combined, node transmission efficiency and priority are dynamically adjusted, transmission path elasticity is enhanced, and resource optimization efficiency is improved. The resource state of the target end is monitored and adjusted in real time, migration stability is ensured, the resource integration precision and service controllability of the multi-cloud environment are improved, and complex application requirements are met.

The invention discloses an AI server intelligent data analysis method based on edge-cloud collaboration, and relates to the technical field of edge-cloud computing collaboration, edge equipment is deployed at an edge end, original data of each task is collected in real time, the data is preprocessed, task difficulty features are extracted, and a task difficulty feature sequence is formed; and based on the task difficulty feature sequence and a pre-trained AI data analysis model, the task difficulty of the collected task data is evaluated in combination with the current resource condition, and the feasibility of task execution at the edge end is judged. According to the method, equipment is deployed at the edge end and data is preprocessed, so that part of computing tasks are effectively unloaded to the edge end, the computing burden of a cloud AI server is remarkably reduced, the edge end can independently complete simple tasks, complex tasks are uploaded to the cloud according to needs, excessive centralized use of cloud resources is avoided, and the computing efficiency is improved. Therefore, the energy consumption and the operation cost of the cloud server are reduced, and the energy efficiency ratio of the whole system is improved.

The invention relates to the technical field of intelligent power grids, in particular to an intelligent design method and system for electric power engineering cloud resources based on distributed computing. Comprising the following steps: constructing an electric power engineering multi-dimensional resource modeling system, dividing calculation nodes into three types of heterogeneous resource units including a real-time processing core, a data analysis core and a disaster recovery backup core, and establishing a dynamic attribute matrix containing time delay sensitivity, an energy consumption coefficient and a risk assessment value; the real-time processing core is configured with a hardware acceleration instruction set and supports microsecond response; a self-adaptive dynamic clustering algorithm is adopted, elastic resource clusters facing task requirements are generated according to the spatial topological relation of resource units and the load change trend, a quantum genetic optimization mechanism is introduced in the clustering process to dynamically adjust the inter-cluster coupling degree, and a cross-cluster communication link based on credibility evaluation is established; according to the design, the problems of low resource utilization efficiency and service quality degradation caused by a static allocation mode can be fundamentally solved.

The invention discloses an intelligent private cloud resource scheduling management system based on a hyper-converged architecture, particularly relates to the technical field of virtual resource scheduling management, and is used for solving the problem of overall utilization rate reduction caused by cascade resource oscillation and fragmentation due to fault recovery in the prior art. Candidate buffer nodes are screened based on the resource occupancy state, a migration priority queue is generated in combination with the node resource isomerism matching degree and the real-time load, and an independent buffer resource pool is reserved at a target node; key resources are directionally reserved through resource locking labels, task dependence periods are associated, and task conflict risks possibly triggered after the resources are released are dynamically predicted; adjusting a label effective period to cover a task execution window based on a conflict identification result; and removing the label in the preset time window, releasing the resources to the buffer pool, synchronously triggering global resource integration and candidate node list updating, and realizing efficient recovery and dynamic redistribution of fragmented resources, thereby inhibiting multi-node resource fluctuation.

A method and system for correcting misaligned states of cloud resources in a cloud infrastructure are provided. The method includes receiving a first list of cloud resources and their current operational states in a cloud infrastructure; retrieving, from at least one IaC tool, a second list cloud resources and their deployed states; for each cloud resource includes in the first list and second list, comparing its current operational state to its deployed state to identify a misaligned state, wherein a misaligned state of a cloud resource is when its current operational state differs from its deployed state; for each cloud resource identified with a misaligned, generating a configuration code to fix the identified misaligned cloud resource; and causing the IaC tool to apply the generated configuration code on each misaligned cloud resource, thereby causing the current operational state to match the deployed state of the misaligned cloud resource.

The invention discloses an automatic resource planning method, system and device for hybrid clouds and a medium, and the method specifically comprises the steps: determining a business change trend through employing a time sequence analysis algorithm, and obtaining a demand prediction model; obtaining dynamic resource demand parameters from the demand prediction model, judging a resource allocation proportion of the private cloud and the public cloud, and determining an initial configuration scheme of a cross-cloud resource pool; based on the initial configuration scheme, a reinforcement learning algorithm is adopted to optimize resource scheduling dynamics, and a real-time scheduling plan is obtained; specific parameters of resource allocation are extracted from the real-time scheduling plan, whether the requirements for safety and flexibility are met or not is judged, and a final scheduling instruction is determined; and based on the final scheduling instruction, generating a configuration script and an automatic deployment file for the cross-cloud environment by adopting a preset template engine. According to the method, the hybrid cloud resource delivery efficiency and the resource utilization rate are improved, and deepening and breakthrough of the hybrid cloud technology in enterprise application are promoted.

Techniques for provisioning a cloud resource instance associated with a virtual cloud network may include detecting a certificate bundle-retrieval trigger during a provisioning process for the cloud resource instance, and responsive to detecting the certificate bundle-retrieval trigger, sending, to an agent executing on a network interface linked to the cloud resource instance, a request for a certificate bundle for the cloud resource instance. Techniques may further include receiving the certificate bundle from the network interface. The certificate bundle may include a set of certificate authority (CA) certificates. Techniques may further include installing the certificate bundle in a storage medium associated with the cloud resource instance. Installing the certificate bundle may represent an operation of the provisioning process.

The invention discloses a dual-module cloud resource prediction method based on deep learning, and belongs to the field of cloud resource prediction. The method aims at solving the problem that in a complex cloud computing environment, an existing cloud resource prediction model is difficult to effectively capture short-term fluctuation and long-term trend at the same time, and consequently resource prediction precision is low. The method comprises the following steps of S1, reading a data set, performing preprocessing, eliminating abnormal values and filling missing values; s2, designing an embedding module to map the preprocessed original data to a high-dimensional vector space; and S3, designing a global module to extract global features in the historical data. And S4, designing a local module to extract local features in the historical data. And S5, designing a fusion module, fusing the outputs of the global module and the local module, and generating a final prediction result. According to the method, a global and local dual-module structure is adopted, and the accuracy and real-time performance of cloud resource prediction are improved through joint learning of global and local module information.

Disclosed are systems, apparatuses, methods, and computer-readable media for inventory monitoring in a multi-cloud network environment. A method includes receiving user input via a controller's interface, wherein the input includes first account information granting access to a first virtual private cloud and a second virtual private cloud, both hosted by a cloud service provider (CSP). The controller queries the first and second virtual private clouds using the respective account information and the CSP's APIs to identify resources, gathering them into an inventory, and maintains a mapping of tags to the resources, associating each tag with a specific security policy. The controller forwards a first subset of tag-resource mappings to a first security gateway in the first virtual private cloud, enabling automatic application of corresponding security policies upon instantiation of tagged resources.

Techniques are described for efficient replication and maintaining snapshot data consistency during file storage replication between file systems in different cloud infrastructure regions. In certain embodiments, provenance IDs are used to efficiently identify a starting point (e.g., a base snapshot) for a cross-region replication process, conserve cloud resources while reducing network and IO traffic.

Systems, methods, and non-transitory, machine-readable media may facilitate testing within sub-environments with respect to resource capacity data corresponding to resources. Specifications of resource allocations for resources may be collected. Observation data, including resource metrics data, may be consolidated and used to develop resource data composites with mapped resource metrics data. A sub-environment interface may be configured to allow creation of sub-environments based on the resource data composites. Source schemas and data structures corresponding to the resource data composites may be selected. A migration plan to migrate the selected data structures to a destination schema for testing in a sub-environment may be created. A migration according to the migration plan may be executed. The destination schema may be created to match the selected source data structures and the selected source schemas. The selected data structures may be migrated to the destination schema to allow for testing in the sub-environment.

Examples analyze effective permissions in a role-based access control system. A prompt is created for a large language model (LLM). The prompt includes a role definition for a role of a role-based access control system, and action definitions. The role definition for the role includes an action and effective permissions text describing a summary of permission limitations provided by the role. The action definitions are provided in a hierarchical format. Query text is added to the prompt. The query text includes a question about effective permissions associated with the role. The prompt is submitted to the LLM, thereby generating response text from the LLM. The response text is displayed to a user.

The invention provides a satellite resource dynamic allocation method under a cloud-native hybrid cloud architecture, and the method comprises the steps: packaging satellite load resources and cloud resources through a containerization technology, so as to form a unified virtual resource pool; constructing a deep learning model, predicting resource demands of each beam coverage area in a future time period based on satellite task historical data and real-time demands, and generating a resource matching scheme; a reinforcement learning algorithm is adopted to dynamically adjust the beam radius, channel allocation and time slot scheduling so as to match the real-time load change of cloud resources; according to the method, a satellite and cloud digital twin model is constructed, a scheduling strategy is simulated and predicted based on a resource matching scheme through the satellite and cloud digital twin model, a resource state is mirrored in real time, and a strategy library is updated according to simulation feedback data of the satellite and cloud digital twin model, so that multi-satellite task automatic recombination is realized. The invention further provides a satellite resource dynamic allocation system under the cloud and native hybrid cloud architecture and a computer storage medium. According to the method, three-dimensional cooperation of dynamic resource pooling, intelligent prediction and elastic scheduling is realized, and the problem that satellite task response time, resource utilization rate and safety cannot be achieved at the same time can be effectively solved.

The invention relates to an electric power virtual simulation optimization method based on a digital twin technology, and the method comprises the following steps: S1, deploying sensors at key nodes of an electric power system, and integrating an intelligent electric meter, weather forecast, social media information and market data to form a comprehensive data pool; s2, constructing a digital twinborn model of the power system; s3, optimizing the digital twin physical model based on a Bayesian optimization method and an adversarial neural network; s4, applying a bee colony algorithm to formulate a cooperation strategy, and optimizing transmission and load adjustment of power in different areas; s5, introducing an adaptive neural fuzzy inference system, and further optimizing a cooperation strategy; and S6, based on the optimized digital twinborn model and the operation strategy, performing large-scale distributed simulation by using cloud resources. According to the invention, accurate construction of the electric power simulation model and accurate implementation of electric power virtual simulation are realized.

Disclosed in the embodiments of the present application are a data access control method and an apparatus, which are used for improving security for data access control. The method in the embodiments of the present application comprises: configuring authentication information for data access control, the authentication information comprising one or more of the following: an identity security classification level, a resource security classification level and an environment security classification level, the identity security classification level being used for indicating the access authority of a user, the resource security classification level being used for indicating the confidentiality level of a cloud resource, and the environment security classification level being used for indicating the confidentiality level of a cloud environment; receiving a data access request from the user, the data access request being used for requesting access to data in the cloud resource or cloud environment; and processing the data access request according to authentication logic, the authentication logic comprising control logic between the authentication information, the authentication logic being used for controlling the access authority of the user to the data in the cloud resource or cloud environment, or being used for controlling the flow direction of the data between the cloud resource or the cloud environment, and the flow direction being a direction from a low security classification level to a high security classification level.

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

The invention discloses a cross-cloud resource scheduling system and method based on deep learning, and the system comprises a resource monitoring service module which is used for monitoring resource information applied to a plurality of cross-cloud platforms, and the output end of the resource monitoring service module is connected to a data preprocessing module; the data preprocessing module carries out preprocessing operation on monitored data and then sends the data to the CNN prediction model, the CNN prediction model processes time series data through multiple convolutional layers based on the preprocessed data and captures resource use trend and periodic changes, and the model outputs resource demand prediction in a period of time in the future. And the output end of the resource scheduling module is connected with the strategy optimization module, the strategy optimization module outputs an optimal resource scheduling strategy to the strategy implementation module through processing scheduling, and the strategy implementation module executes the resource scheduling strategy. According to the scheme, real-time dynamic resource scheduling can be realized to meet the requirement of cross-cloud platform resource scheduling.

Systems, apparatus, articles of manufacture, and methods are disclosed that monitor and correct for configuration drift in cloud accounts by instantiating or executing machine-readable instructions to access target state configuration information from a first user account for a cloud account, onboard the cloud account at a first time to configure cloud resources based on the target state configuration information, detect a first drift between the target state configuration information and an in-use configuration state of the cloud account at a second time, log a corresponding change in the in-use configuration state relative to the target state configuration information, the first event record logged in a timeline of second event records representing second drifts of the cloud account relative to the target state configuration information, and after the detection of the first drift, change an in-use configuration of the cloud account based on the target state configuration information.

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

The invention relates to a method and a device for tracking a dynamic resource dependency relationship and terminal equipment. The method comprises the following steps: acquiring cloud resource dependency data of a cloud service platform and / or a data center in real time; constructing a cloud resource dynamic dependency graph according to the cloud resource dependency data; detecting the cyclic dependence of the cloud resource dynamic dependence graph by adopting a depth-first search algorithm to obtain a cyclic dependence detection result; and using a TGCN algorithm to predict the future cyclic dependence of the cloud resource dynamic dependence graph to obtain a cyclic dependence prediction result. According to the method, a real-time cloud resource dynamic dependency graph is constructed, a depth-first search algorithm and a TGCN algorithm are adopted to process the cloud resource dynamic dependency graph to obtain a cyclic dependency detection result and a cyclic dependency prediction result, and a basis is provided for determining corresponding resource cyclic dependency prevention measures and processing strategies. And data is provided for effective cloud resource management and optimization, so that problems and risks caused by cyclic dependence are reduced, and the reliability of cloud services is improved.

The invention relates to the technical field of industrial operation, and discloses a multi-interface industrial control device based on an OpenHarmony industrial operating system and a control method thereof, and the method comprises the steps: scanning an interface resource list of an industrial control computer after the OpenHarmony industrial operating system is started; industrial equipment connected to each interface is automatically found according to the interface resource list, and an equipment connection table is obtained; performing protocol conversion based on the device connection table to obtain a device communication data stream, and extracting a feature data set from the device communication data stream; and calculating an interface coordination scheme based on the feature data set, issuing a control instruction to each industrial device, and collecting execution feedback to obtain unified management and control data. Unified control and remote cooperation of the industrial control equipment are realized through the OpenHarmony distributed soft bus, the conflict problem during multi-protocol concurrent access is effectively solved, and local intelligent decision and cloud resource scheduling of the industrial operation system are realized.

Systems and methods of the disclosure include: identifying, by a cloud resource management system, a cloud resource consumption model associated with one or more cloud resources; generating, by the cloud resource management system, a sequence of instructions implementing a smart contract based on the cloud resource consumption model; sending, to a distributed ledger network, the smart contract; receiving, by the cloud resource management system, cloud resource usage data associated with the one or more cloud resources; and causing, by transmitting a message reflecting the cloud resource usage data to the distributed ledger, the smart contract to be executed.

The invention discloses a hybrid cloud architecture-oriented heterogeneous resource automatic discovery and integrated configuration method, and relates to the technical field of integrated configuration. Comprising the following steps: automatically identifying and dynamically adjusting a resource detection frequency to capture new resources through an adaptive learning and dynamic optimization mechanism; a deep learning model is utilized to predict a resource state change trend, detection is initiated in advance, and real-time monitoring and active response are realized; a dynamic API adaptation strategy and a self-learning mechanism are introduced, and cross-platform API intelligent adaptation and calling success rate optimization are achieved; a self-adaptive event-driven detection and multi-modal feature fusion method is adopted, and the detection frequency is dynamically adjusted based on anomaly detection; through a multi-modal attribute fusion and attribute mapping and conversion method, a unified resource description model is constructed, and standardization and consistency of resource attributes are carried out. According to the method, the operation is ensured to accord with laws and regulations such as GDPR, HIPAA and the like, and the validity of the operation and the consistency of the international standard are ensured while efficient collaboration and dynamic load balancing of cross-cloud resources are realized.

Systems, methods, and apparatus are provided for a secure multicloud test automation framework. The automation framework may receive a natural language request for a cloud-based test scenario at a user interface on an enterprise network. The test scenario may include a cloud resource and a cloud platform. Machine learning may map keywords extracted from the test scenario to a set of test cases associated with the cloud resource. Keyword mapping may also identify a configuration file associated with the cloud platform. The automation framework may retrieve shell scripts specified by the set of test cases from a shell script repository. The automation framework may use data from the configuration file to access the cloud platform via a secure gateway and execute the shell scripts using data from the cloud platform. The automation framework may generate a comprehensive summary report and maintain technical logs for the test.