19 results about "Elastic cloud" patented technology

The invention provides a carbon satellite application cloud resource dynamic allocation method based on data volume prediction, and relates to the crossing field of cloud computing and remote sensing satellites. A fine-grained process monitoring module and an application feature labeling module are used for respectively acquiring a plurality of process performance data and a plurality of task feature tags of a plurality of data processing tasks in a carbon satellite application system; and performing cloud resource dynamic optimization and outputting an elastic cloud resource optimization strategy to dynamically adjust the resource ratio of the basic computing power resource pool to a plurality of data processing tasks for pre-allocation. The technical problems that the GPU / CPU utilization rate is in an extremely unbalanced state and a high-priority task is interrupted due to the fact that supply and demand of computing power resources are unbalanced in a carbon satellite service system in an existing resource allocation method in a cloud environment are solved. The technical effect of zero interruption of a high-priority carbon emission data analysis task is guaranteed while accurate matching of resource supply and demand is realized and the resource utilization rate is improved.

In one embodiment, a device obtains information regarding a plurality of network nodes of a network via which an online application is accessed. The device uses a prediction model to predict a quality of experience metric for the online application. The device determines, a topology change for the network, based on the quality of experience metric predicted for the online application and the information regarding the plurality of network nodes. The device causes the topology change to be implemented in the network.

The invention discloses an after-class service business management method and system based on a SaaS mode, and relates to the technical field of after-class services, and the method comprises the steps: distributing an independent database fragment for each education end based on a SaaS multi-tenant isolation strategy; dynamically generating an electronic protocol template according to the course type and the real-time site capacity, filling parent information by using a placeholder replacement rule, and generating a to-be-signed document; asynchronously pushing the to-be-signed document according to the priority of the tenant through the message middleware and the elastic cloud server cluster; receiving the electronic signature completed by the parent end, and fusing the electronic signature with the to-be-signed document to generate an electronic protocol; and when the agreement signing completion rate exceeds a preset threshold value, generating a scheduling scheme by using a resource scheduling model in combination with the teacher qualification matching degree, the classroom capacity limitation and the course time continuity constraint.

The invention relates to the technical field of digital human synthesis, in particular to a digital human synthesis system based on an interactive multimedia editing and cloud rendering technology, which comprises a multi-mode interactive editing front end, and the multi-mode interactive editing front end is coupled with a distributed action synthesis engine. The distributed action synthesis engine is coupled with an elastic cloud rendering cluster, and the elastic cloud rendering cluster is coupled with an intelligent synchronous controller. According to the digital human synthesis system based on the interactive multimedia editing and cloud rendering technology, through triple innovation of algorithm-architecture-theory, the end-to-end delay is less than 80ms under film and television level image quality, and a performance benchmark is established for next-generation digital human application; the problem that in the actual use process of a digital man-machine synthesis system in the prior art, when a GPU conducts path tracking under the 4K resolution, the single-frame rendering time exceeds 500 ms, and the 100 ms threshold value of real-time interaction cannot be met is solved.

Aspects of the disclosure are directed to dual layer load balancing. In accordance with one aspect, the disclosure includes executing a first layer network load balancing strategy for a client server task using a network load of each of a plurality of hubs; obtaining a network load of cloud terminal resources in a hub center on a hub-terminal side for the client service task; and executing a second layer network load balancing strategy for the client service task using the network load of the cloud terminal resources to select one hub.

1. Name of the product in this design: Graphical User Interface for Elastic Cloud Servers for Electronic Devices. 2. Intended use of this design: for use in an electronic device. 3. The key design feature of this product is its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Electronic devices are designed according to convention, and other views of each design are omitted. 6. Design 1 is designated as the basic design. 7. Purpose of the graphical user interface: For use with elastic cloud servers. 8. Human-computer interaction methods of the graphical user interface: Design 1's main view interface is the login interface. After logging in by entering the account and password, the interface changes as shown in Figure 1. Selecting "Elastic Cloud Server (ECS)" in the overview navigation at the top left of Figure 1 leads to Figure 2. Selecting "Name / ID" from the list in Figure 2 leads to Figure 3. Design 2's main view interface is also the login interface. After logging in by entering the account and password, the interface changes as shown in Figure 1. Selecting "Elastic Cloud Server (ECS)" in the overview navigation at the top left of Figure 1 leads to Figure 3. To access the Elastic Cloud Server (ECS) interface, select "Name / ID" from the list in Design 2 and then select "USB Device" to access Design 2 interface (Figure 3). The main view of Design 3 is the login interface. After logging in with your account and password, the interface shown in Design 3 interface (Figure 1) will appear. Select "Elastic Cloud Server (ECS)" from the overview navigation in the upper left corner of Design 3 interface (Figure 1) to access Design 3 interface (Figure 2). Select "Name / ID" from the list in Design 3 interface (Figure 2) and then select "Event List" to access Design 3 interface (Figure 3). 9. Other situations requiring explanation: Other explanation: The single gray filled area in the interface is the content screen.

The invention discloses a rail transit data analysis method and system based on cloud computing, and relates to the technical field of rail transit data analys.The rail transit data analysis method comprises the steps that global operation data of a target rail transit network is collected, a dynamic panoramic topological graph is constructed based on the global operation data, the dynamic panoramic topological graph is divided into N traffic area sub-graphs, and the N traffic area sub-graphs are used for analyzing the traffic area sub-graphs; configuring elastic cloud computing nodes mapped to the N traffic area sub-graphs, distributing global operation data to the corresponding elastic cloud computing nodes, performing global operation data initialization screening and cloud association conversion through a data clustering algorithm in combination with a cloud computing processing model, determining abnormal data distribution, and after a cloud platform monitors a key event, sending the key event to the cloud platform. Summarizing the abnormal data of each elastic cloud computing node, mining the generality of the abnormal data, determining and predicting the distribution of the abnormal data in combination with the real-time passenger flow evolution trend, obtaining an abnormal distribution diagram, mapping the abnormal distribution diagram in the dynamic panoramic topological graph in real time, and generating global early warning information.

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for allocating computation resources for a plurality of databases. For each database, the system identifies a respective initial computation capacity tier for the respective database based at least on the respective utilization of the respective database. For each of a set of optimization orders, the system determines a respective set of candidate resource pools for accommodating the plurality of databases. The system selects an optimization order and determines a final set of resource pools for the plurality of databases. The system outputs data specifying the final set of resource pools.

The application discloses a kind of rail transit data analysis method and system based on cloud computing, it is related to rail transit data analysis technical field, the global operation data of target rail transit network is collected, dynamic panoramic topological graph is constructed based on the global operation data, dynamic panoramic topological graph is segmented into N traffic area subgraph, the elastic cloud computing node mapped to the N traffic area subgraph is configured, global operation data is distributed to corresponding elastic cloud computing node, in combination with cloud computing processing model, global operation data initialization screening and cloud association conversion are carried out by data clustering algorithm, determine abnormal data distribution, after cloud platform monitors key event, the abnormal data of each elastic cloud computing node is summarized, the commonality of abnormal data is mined, in combination with real-time passenger flow evolution trend determines predicted abnormal data distribution, obtains abnormal distribution graph, and abnormal distribution graph is mapped in dynamic panoramic topological graph in real time, generates global early warning information.

Methods and system to dynamically tune an application operating on a cloud computing infrastructure. The dynamic tuning generates a dependency graph based on the applications configurable parameters while operating on cloud computing infrastructure. One or more subgraphs are selected from the dependency graph which include a subset of the parameters of the dependency graph, and corresponding subgraph variations are created. The subgraphs and subgraph variants undergo a simulation used to identify a subset of the subgraphs with viable operation. This subset of subgraphs undergoes probabilistic modelling to generate a ranked set of configurations, which are then implemented in test replicas of the application and evaluated against the production replica. Once the test replicas are determined to outperform the production replica, they are implemented within the production replica.

The invention discloses an elastic cloud platform resource dynamic scheduling and multi-mode disaster recovery method for a smart scenic spot. The method comprises the following steps: 1) performing multi-dimensional load prediction and elastic scaling; 2) a cross-regional multi-activity disaster recovery architecture; and 3) unified access and management of the Internet of Things equipment. According to the method, multi-source information such as historical passenger flow data, Internet of Things sensor real-time flow and meteorological data is deeply fused, and the business load fluctuation in the next 24 hours is accurately predicted. And dynamic adjustment of the number of virtual machine instances and the container resource quota is realized by means of a Kubernetes cluster. Meanwhile, a cross-regional multi-activity disaster recovery system is built by adopting a double-center and edge node architecture, database transaction-level real-time synchronization is realized by applying an OGG technology, a robus load balancing layer is built by combining Nginx and Keepalived, and seamless switching of services within less than or equal to 3 seconds when a main center fails is ensured. According to the method, the service continuity, the resource elasticity and the operation and maintenance efficiency of the scenic spot are remarkably improved.

In one embodiment, a device obtains information regarding a plurality of network nodes of a network via which an online application is accessed. The device uses a prediction model to predict a quality of experience metric for the online application. The device determines, a topology change for the network, based on the quality of experience metric predicted for the online application and the information regarding the plurality of network nodes. The device causes the topology change to be implemented in the network.

Methods, apparatus, systems, and articles of manufacture providing a tiered elastic cloud storage to increase data resiliency are disclosed. An example instructions cause one or more processors to at least execute the instructions to: generate a storage scheme for files based on a categorization of the files and resource capabilities of an edge-based device and a cloud-based device, the categorization including a first group of files to be stored locally at an end user computing device, a second group of files to be stored externally at the edge-based device, and a third group of files to be stored externally at the cloud-based device; in response to an acknowledgement from at least one of the edge-based device or the cloud-based device, generate a map corresponding to locations of the files; store the first group of files in local storage; and cause transmission of the second group of files to the edge-based device and the third group of files to the cloud-based device.

The present application relates to the field of intelligent agricultural technology, and more particularly to a greenhouse environment big data acquisition and regulation method based on edge-cloud cooperation. The present application collects environment, crop physiology, soil moisture and energy consumption parameters by deploying a multi-modal heterogeneous sensor network, performs spatio-temporal alignment, anomaly detection, PCA dimension reduction and improved D-S evidence theory fusion by the edge gateway, generates a low-dimensional comprehensive feature vector, and makes dynamic offloading decisions according to task timeliness, network bandwidth and edge load; real-time emergency tasks call lightweight models on the edge to achieve millisecond-level inference and regulation; short-period tasks are uploaded to the cloud, and a hybrid digital twin model is used to predict environmental trends and generate pre-regulation strategies; long-period tasks aggregate encrypted gradients from multiple greenhouses through federated learning to achieve cross-greenhouse knowledge sharing. The present application realizes high-dimensional data acquisition, elastic cloud-edge cooperation, active prediction and regulation, and cross-greenhouse privacy cooperation, significantly improving the intelligent level of greenhouse environment regulation.

The application provides a carbon satellite application cloud resource dynamic allocation method based on data volume prediction, relates to the cross field of cloud computing and remote sensing satellites, and collects process performance data and task characteristic labels of multiple processes of multiple data processing tasks in a carbon satellite application system through a fine-grained process monitoring module and an application feature labeling module, respectively, performs cloud resource dynamic optimization, outputs an elastic cloud resource optimization strategy, and dynamically adjusts the resource allocation of a basic computing resource pool to multiple data processing tasks for pre-allocation. The technical problems of the carbon satellite business system in the existing resource allocation method under the cloud environment, that is, the GPU / CPU utilization rate is in an extreme imbalance state due to the imbalance between supply and demand of computing resources, and high-priority tasks are interrupted. In the implementation of accurate matching of resource supply and demand, the technical effect of improving resource utilization rate while guaranteeing zero interruption of high-priority carbon emission data analysis tasks.

This invention relates to the field of non-destructive testing technology for power equipment, specifically disclosing an ultrasonic testing method for insulation defects in cable terminals. This method excites shear waves in the insulating material using focused ultrasonic pulses, synchronously receives the echo signals using a multi-channel ultrasonic array, calculates the shear wave propagation velocity, and inverts the spatial distribution of the elastic modulus to construct a three-dimensional elastic cloud map. Then, combined with a pre-set constitutive mapping model, the hardness distribution is transformed into an assessment result of the insulation performance degradation state. By employing the above technical solution, this invention can quantitatively characterize the non-uniformity of material mechanical properties, predict potential failure areas before defects appear, improve the detection capability and assessment accuracy of early insulation degradation, and provide a reliable basis for preventive maintenance of power equipment.

The application provides a method for realizing cloud-outside bare metal access to VPC, comprising the following steps: S1, the bare metal server is first physically connected with the interface of the leaf switch, and then the connected interface is set as a two-layer access port; S2, a user creates a two-layer access instance to bind the cloud-outside bare metal server with the interface of the leaf switch in the cloud, and sets a tag or untag mode; S3, when the user creates the two-layer access, one or more mapping relations can be created under the instance; S4, when the user deletes the mapping relation, the vlan is unreserved, and the service-instance configuration is deleted; S5, when the user deletes the two-layer access instance, the instance is allowed to be deleted only when there is no two-layer mapping relation under the instance, and the deletion of the instance will unbind the relation between the bare metal and the interface of the leaf switch in the vpc. The application has the beneficial effects that the cloud-outside bare metal server accesses the VPC in the cloud, and the bare metal interworks with the elastic cloud host or the bare metal server in the cloud.