91 results about "Cloud resources" patented technology

Systems and methods for selectively updating permissions associated with a cloud resource deployment are disclosed. An example method includes receiving a first request to deploy first target cloud resources based on a first specified state defined in a configuration repository, selectively updating deployment permissions associated with the first specified state and deploying the first target cloud resources based at least in part on the first specified state and the updated deployment permissions.

The application relates to a cloud service-based model inference service system and method capable of on-demand scaling. The system comprises a gateway module, a model inference service management module and a request distribution module. The gateway module is used for receiving a user request and extracting heterogeneous features representing the request's demand for computing resources. The model inference service management module is used for generating resource scheduling instructions for each inference service instance based on the multi-dimensional indicators of the deployed inference service instances, in combination with the resource cost and availability information of each cloud resource pool. The request distribution module is used for distributing the user request to the inference service instance that is adapted to the heterogeneous features according to the resource scheduling instructions. The system can solve the problems of low utilization rate of computing resources, poor request adaptability and lack of cross-cloud scheduling in the prior art.

The application discloses a game processing method and device, electronic equipment and a storage medium, comprising: in response to triggering an authentication operation for a target cloud game in a target application subroutine, obtaining authentication data; sending the authentication data to an authentication server to determine an authentication result based on the authentication data through the authentication server, and generating target authentication credential information corresponding to the target cloud game according to the authentication result; receiving the target authentication credential information returned by the authentication server, and generating a game start request based on the target authentication credential information; sending the game start request to a cloud game server; receiving the media stream transmitted by the cloud game server to display the media stream through the target application subroutine. The application decouples the login process and the cloud resource allocation process, significantly reduces the idle loss of cloud computing resources, improves the utilization rate of computing resources, and significantly reduces the overall operation cost and resource overhead of cloud games.

An edge device performs automated visual inspection on a production line. A sequence of item images is received, and patch-level feature embeddings are extracted for each image. During live operation, a memory bank modeling normal behavior is built by computing distances between patch embeddings and existing entries and appending embeddings whose distance exceeds a dynamic add-threshold derived from a running mean and standard deviation of prior distances. For a candidate image, nearest-neighbor distances from its patches to the memory bank are computed and aggregated into an image-level anomaly score. A prediction threshold is calibrated by synthesizing defects from normal images, scoring both perturbation-augmented and normal images to obtain score distributions, and selecting a threshold that discriminates between them. The candidate image is classified as defective or normal by comparing its anomaly score to the calibrated threshold. All computation executes on the edge device without reliance on cloud resources during operation.

The application provides a mode switching method, device, storage medium and program product, which are applied to a management and control server. The method comprises the following steps: acquiring a mode switching request sent by a client, wherein the mode switching request is used for indicating a target instance to be switched and a target mode; wherein the target instance is any instance in at least one instance of the client, and the target mode is any mode in multiple modes selectable by the client; and cloud resources matched with the target mode are configured for the target instance. The use experience of a user is improved.

A hybrid forecasting system (100) for tiered cloud pricing using ensemble learning, comprising: a data ingestion module configured to continuously ingest and aggregate heterogeneous data from a variety of sources, including historical cloud usage data, real-time resource consumption metrics, customer subscription profiles, service-level agreement parameters, and external demand indicators; a preprocessing engine functionally coupled with the data ingestion module, the preprocessing engine being configured to perform data cleansing, normalization, transformation, feature extraction, and dimensionality reduction to generate structured and model-compatible datasets;a model training unit that is functionally coupled with the preprocessing engine, wherein the model training unit comprises a variety of heterogeneous predictive models, including at least one statistical model, at least one machine learning model, and at least one deep learning model, each configured to process the structured datasets independently to generate predictive results that meet future cloud resource needs, workload variability, and price sensitivity across multiple service tiers;an ensemble aggregation layer that is functionally coupled to the model training unit, wherein the ensemble aggregation layer is configured to receive and combine the forecast results generated by the multitude of forecasting models using ensemble learning techniques, including weighted averaging, stacking or boosting, with the weights assigned to each forecasting model being dynamically adjusted based on predefined performance evaluation metrics to generate a uniform and optimized forecast;a price optimization engine that is functionally connected to the ensemble aggregation layer, wherein the price optimization engine is configured to determine and dynamically adjust tiered price structures based on the unified forecast, taking into account parameters such as forecasted demand, user segmentation, demand elasticity, infrastructure capacity constraints, and predefined optimization goals such as revenue maximization and resource utilization efficiency;and a feedback adjustment module that is functionally coupled with the price optimization engine and the model training unit, wherein the feedback adjustment module is configured to monitor system performance in real time, user response to price adjustments and resource utilization results, and iteratively updates model parameters and pricing strategies using adaptive learning mechanisms, including reinforcement learning, wherein the system (100) is configured to operate in a multi-tenant cloud environment, supports real-time data processing and decision-making, and enables automated, scalable, and adaptive optimization of tiered cloud pricing.

This invention discloses a ship cloud computing load balancing method based on a hybrid intelligent algorithm, relating to the field of ship intelligent computing and cloud computing integration technology. The method includes the following steps: task classification modeling, edge-cloud resource status monitoring, multi-objective optimization decision-making, and dynamic load adjustment. This invention reduces the load standard deviation of ship edge-cloud nodes through a multi-objective optimization algorithm and a dynamic adjustment mechanism. It optimizes task processing efficiency, reducing the average latency of real-time tasks and shortening the total processing time of non-real-time tasks. It reduces system energy consumption by optimizing the energy consumption coefficient, thereby reducing the total energy consumption of edge nodes and cloud servers. It adapts to ship scenarios, improving the robustness and adaptability of scheduling strategies to address the heterogeneity of ship tasks and network dynamics.

This invention discloses an edge-cloud resource allocation method and system, applied in the field of edge-cloud resource allocation technology. The method includes: acquiring real-time operating data of insulators; determining each electric field analysis task and its priority based on anomaly analysis results of the real-time operating data; determining the edge-cloud resource requirements of each electric field analysis task based on the simulation processing results of each task; determining the resource scarcity of the electric field analysis system; determining an initial edge-cloud resource allocation strategy based on the edge-cloud resource requirements and task priorities corresponding to each electric field analysis task; optimizing the initial edge-cloud resource allocation strategy using a multi-objective optimization algorithm to obtain a target edge-cloud allocation strategy; and allocating corresponding edge-side resources and cloud-side resources to each electric field analysis task based on the target edge-cloud allocation strategy. The edge-cloud resource allocation method and system provided by this invention effectively improve the utilization efficiency of edge-cloud resources.

This application discloses a logical node management method and cloud system based on cloud computing technology, belonging to the field of cloud service technology. The method includes: a cloud management platform creating a target logical node in the infrastructure, wherein the target logical node includes a virtual CPU, a first virtual extended processor, and a virtual bus network, the virtual CPU and the first virtual extended processor being logically connected to the virtual bus network respectively; if the cloud management platform confirms that at least one first extended processor in the extended processor device pool has failed, it unloads the first virtual extended processor from the target logical node; the cloud management platform then attaches a second virtual extended processor to the target logical node, the second virtual extended processor implementing all or part of the functions of at least one second extended processor in the extended processor device pool, the second virtual extended processor being logically connected to the virtual bus network. This application improves the utilization rate of cloud resources.

The application provides a computing resource allocation method and device, electronic equipment, medium and product. The method comprises the following steps: receiving a computing resource allocation request sent by a target user end; determining a target resource type according to a target tenant identifier, wherein the target resource type comprises a private cloud resource or a public cloud resource; determining a target computing node from a private cloud and / or a public cloud according to the target resource type and a target resource quantity through a scheduling algorithm, wherein the target computing node comprises computing resources with a quantity greater than or equal to the target resource quantity; determining target interface information corresponding to the target computing node, and sending the target interface information to the target user end, so that the target user end uses the computing resources of the target computing node according to the target interface information. According to the above scheme, the target computing node corresponding to the target resource type is determined through the scheduling algorithm, and the computing resources of the target computing node match the target resource type of the target user end, thereby improving the accuracy of allocating computing resources.

The application discloses a cloud resource deployment method, device, equipment and computer storage medium, relates to the technical field of cloud computing, and comprises the following steps: acquiring a cluster configuration file, a cluster parameter template and an application configuration file; creating a cloud resource generation instance according to the cluster configuration file and the cluster parameter template, and creating a cluster through the cloud resource generation instance; establishing a mapping relationship between the cloud resource generation instance and the cluster; determining the cloud resource generation instance of the cluster corresponding to the application configuration file according to the mapping relationship; and deploying the cloud resource of an application in the cluster through the cloud resource generation instance according to the application configuration file. According to the application, the cloud resource is automatically configured through the creation of the cloud resource generation instance, automatic application deployment and operation and maintenance management can be realized, and the efficiency of cloud resource deployment is improved.

This application discloses a resource task scheduling method, apparatus, electronic device, and program product, belonging to the field of cloud-edge collaboration technology. The method includes: dividing available cloud resources into a first type of virtual container and a second type of virtual container; predicting the target available resources based on the historical available resources of the edge cluster; combining the target available resources, the resources of the first type of virtual container, and the amount of task data to obtain the predicted task completion time for both; with the goal of having the same unit task processing time, splitting the task data and allocating it to the edge cluster and the first type of virtual container for parallel execution; and dynamically adjusting resource allocation or task splitting boundaries with the goal of minimizing the maximum task completion time, until a preset scheduling stop condition is met. In the event of an edge cluster failure, unfinished tasks are scheduled to the second type of virtual container for continued processing. This application achieves efficient collaboration between cloud and edge resources, effectively improving resource utilization and ensuring the stability and timeliness of task execution.

Embodiments of the present application provide a resource reservation method and a resource allocation method, a device, a storage medium and a program product. In the embodiments of the present application, cloud resources reserved for multiple clients are unified into a reserved resource pool, and the reserved resource pool is shared by the multiple clients, so that the binding relationship between the cloud resources and the clients can be eliminated, the reserved cloud resources in the reserved resource pool can be reused by the multiple clients, the utilization rate of the cloud resources is improved, and the resource selling rate of a cloud vendor is improved. In the resource reservation, the overall use condition information of the current cloud resource reservation is reasonably predicted by learning the historical information related to the cloud resource reservation of the multiple clients, and the overall resource reservation quantity of each resource specification is determined in combination with the resource specification and the resource quantity of the cloud resources reserved by the multiple clients, so that the reserved resource quantity can be saved, and the resource reservation cost is reduced.

The method belongs to the technical field of panoramic monitoring of ultra-high voltage converter station, and aims to solve the problem that panoramic monitoring image data is directly uploaded to the cloud, occupying a large amount of cloud resources. By adopting multi-scale convolution blocks in the deep multi-scale residual network model to construct low-order and high-order features of images of various scales, the incomplete phenomenon of image detail extraction is avoided, and the residual learning mechanism is adopted to retain low-order rough features, thereby improving the reconstruction ability of the image. Topology optimization constructs the topology structure of the heterogeneous network, and the framework combining deep reinforcement learning and Monte Carlo tree search is used to construct the network according to the pre-defined topology rules. The search result of the Monte Carlo tree strengthens the learning of the deep convolutional neural network, so as to obtain more accurate prediction in the next iteration. After the data is processed in the edge side, it is transmitted to the cloud storage, saving the cloud storage space and transmission bandwidth.

The embodiment of the application provides a service optimization method, system, device, computer program product and storage medium. A new decision basis, i.e., stability description information in the unit of cloud resources, is introduced in the service optimization process. The stability description information in the embodiment of the application is in the unit of cloud resources, so that the fine granularity of the stability description information can reach the cloud resource level. Accordingly, in the process of service optimization, the stability of the cloud computing system itself can be improved, and different cloud resources can more accurately avoid stability problems, so that the influence of the stability problems in the cloud computing system on the cloud resources is reduced, and the service stability perceived by the user side can be effectively improved.

Provided are a cloud application deployment method, a cloud application deployment apparatus and a computing device cluster. The cloud application deployment method is applied to a cloud management platform. The method comprises: a cloud management platform acquiring a resource request inputted by a user; on the basis of the resource request, determining a target region from among a plurality of regions, wherein cloud resources in the target region meet the resource request; and deploying a target cloud resource in the target region. The resource request may comprise a category, a specification and / or a service indicator of the target cloud resource. In the method, a user is not required to sense each region in a cloud platform, and a cloud management platform automatically performs region selection on the basis of a resource request of the user, such that resources of each region can be maximally utilized, and the resource utilization rate is thus improved. Moreover, the method shields the user from differences between regions in the cloud platform, thereby realizing regionlessness. In other words, the user is not required to select a region when deploying a cloud application, such that the difficulty for the user in deploying the cloud application can be reduced.

This invention discloses a cloud resource alarm integration and optimization method based on multi-dimensional data, comprising the following steps: Step 1: constructing a cloud resource retrieval model; Step 2: establishing and storing historical alarm data; Step 3: historical alarm extraction; Step 4: multi-dimensional feature iterative update; Step 5: intelligent hierarchical classification; Step 6: deduplication post-processing; Step 7: real-time alarm weighted matching; and Step 8: alarm topology convergence. The invention also includes a cloud resource alarm integration and optimization device based on multi-dimensional data, comprising a topology model storage module, a topology model generation module, a historical alarm database, a historical alarm extraction unit, a reference alarm update module, a classification module, a post-processing module, a sorting and integration module, a topology convergence and positioning module, and an alarm output module. This invention, by constructing a three-layer cloud resource topology retrieval model, automatically merges and batch-converges alarms, and, combined with a dynamic suppression strategy for high-frequency repetitive alarms, can significantly reduce the number of redundant alarm pushes.

This application discloses a cloud resource allocation method in a cloud computing environment, the cloud computing environment including a host and multiple virtual machines / containers, the multiple virtual machines / containers being used to deploy and run cloud computing applications. The method includes: obtaining system metrics of the host and the multiple virtual machines / containers when the cloud computing application runs on the multiple virtual machines / containers; using the system metrics of the host and the system metrics of the multiple virtual machines / containers to perform reinforcement learning to obtain resource allocation strategies for the multiple virtual machines / containers, the resource allocation strategies for the multiple virtual machines / containers being used to represent the resource allocation of the respective virtual machines / containers. This application also discloses electronic devices and storage media. This application improves the resource management performance of dynamic cloud computing.

This invention provides an intelligent UAV patrol method and system based on edge-cloud collaboration in the field of UAV perception and artificial intelligence. The method includes: Step S1, the UAV collects visible light video and infrared video and inputs them into a multimodal fusion perception model to obtain fusion perception results; Step S2, an edge-cloud collaboration mechanism is automatically triggered based on uncertainty scoring, or control commands are generated; Step S3, the ground station gathers the basic target data carrying temporal information transmitted back by the UAV and inputs it into a spatiotemporal attention model to obtain atomic event recognition results, which are then uploaded to the server; Step S4, the server inputs the atomic event recognition results and image slices uploaded by the edge-cloud collaboration mechanism into an anomaly event detection model to obtain anomaly event detection results. The advantages of this invention are: it greatly improves the UAV's accurate perception capability throughout the entire time in wide-area complex scenarios, the interpretability and understanding capability of long-term events, and the dynamic collaborative efficiency of edge-cloud resources.

This invention relates to the field of port data processing, and more particularly to a green port energy digital management platform based on digital twins. When constructing the digital twin, this invention utilizes an incremental synchronization mechanism and spatiotemporal event identifiers to ensure that the green port energy digital management platform only transmits incremental data that has changed. This not only saves on expensive cloud resources and dedicated line bandwidth costs, but also allows the green port energy digital management platform to maintain smooth operation even when dealing with massive amounts of data. Furthermore, through the incremental synchronization mechanism, this invention ensures that the green port energy digital management platform only updates the digital twin when changes occur, and combined with an efficient event bus, latency is drastically reduced from minutes to seconds.

This invention discloses an auto-scaling system and method for cloud-native intelligent typesetting services. The system includes a master resource scheduler: for receiving service requests, determining whether current cluster resources are sufficient and automatically scaling, and for phased distribution of service requests; and slave resource schedulers: for receiving phased distributed service requests, determining whether the resources within the containers providing the service locally are sufficient and automatically scaling, and for feeding back request results and resource usage to the master resource scheduler. This invention utilizes a planning algorithm to predict service deployment methods and a search algorithm to predict whether service deployment methods will lead to excessively long service response times. While ensuring timely service completion, it rationally and efficiently utilizes cloud resources. Combined with deployed services, this invention performs targeted automated deployment of microservices under known microservice processes and estimated resource consumption and service duration, while also considering cloud resource utilization and timely scaling up or down cloud resources.

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.

A software module running method includes obtaining a running request for a first software module, where the first software module is associated with a first user, the running request for the first software module is to run the first software module that has been deployed in a first computing instance set, or deploy the first software module in a provisioned first computing instance set for running, or provision a first computing instance set for the first software module and run the first software module by using the first computing instance set, and the first computing instance set includes at least one computing instance; and obtaining a first cloud resource authorization specification corresponding to the first software module.

Methods and systems for managing heterogeneous cloud resources are provided. A normalized data model is created by transforming provider-specific resource definitions into defined schemas while maintaining relationships between components. The normalized data model is stored in a database. Upon receiving a service request, a subset of provider-specific resources is orchestrated based on the normalized model. The database is then updated to reflect changes from the orchestration. This approach enables efficient management and orchestration of diverse cloud resources across multiple providers through a unified data model and orchestration process

The present disclosure provides an access control system, method, device, computer program product and storage medium. A virtual private network to be controlled and a forwarding component to be interconnected in different virtual private networks are set in any firewall engine, and an access rule that is prohibited from being tampered with is set in any forwarding component; in this way, the firewall engine can interconnect with different virtual private networks through the forwarding component, and control access to cloud resource access requests by means of the access rule set in the forwarding component. Accordingly, based on the access control system proposed in the present embodiment, in the case of needing to control access to multiple virtual private networks, the cloud resource access requests can be centrally controlled by the firewall engine deployed outside the multiple virtual private networks, without the need to deploy a firewall engine in each virtual private network, thereby effectively saving the use cost of the firewall engine.

This invention proposes a method for monitoring network signal operation. By deploying a centralized management service in a cloud computing environment, it performs threshold judgments on aggregated signal data, dynamically acquires additional cloud resources to support monitoring, and simultaneously constructs a cloud-based signal monitoring dashboard to update remote signals in real time, identify abnormal patterns, and integrate them into the network monitoring system. For anomalies related to resource scheduling, it obtains optimized paths, enhances dashboard functionality, and ultimately adjusts management services based on communication feedback data. This method achieves efficient aggregation of signal data, accurate identification of anomalies, and intelligent resource scheduling, significantly improving the reliability and response speed of cross-regional communication monitoring and providing stable support for multi-regional network operation.

The present disclosure provides a resource calling method and device, electronic equipment and computer readable storage medium, and can be used in the technical field of cloud management and also can be used in the field of financial technology. The resource calling method comprises the following steps: obtaining resource configuration information and resource allocation rules associated with a target business system; determining a resource allocation result associated with the target business system according to the resource configuration information and the resource allocation rules; and calling a target cloud resource according to the resource allocation result, so that the target business system executes a target business by using the target cloud resource.

Techniques are disclosed for restricting operations between two attached two compute instances. An infrastructure and a generalized method is described for attaching two or more cloud resources (e.g., two compute instances) in spite of the compute resources being provisioned by two different services from different cloud tenancies, and then modifying the allowed operations that can be performed due to the attachment.