Abstract: According to a solution provided in this application, a data write rate of a target index can be calculated, and when the data write rate falls outside a target rate range, a quantity of resources of nodes occupied by a plurality of shards of the target index can be dynamically adjusted. Because both a data storage capacity of the target index and a maximum data write rate supported by the target index are related to the quantity of resources occupied by the plurality of shards, in the solution provided in this application, the data storage capacity and the maximum data write rate of the target index can be dynamically adjusted without changing a quantity of shards included in the target index. In this way, flexibility of data writing is effectively improved, and utilization of cluster resources is effectively improved.

Abstract: A positive electrode plate includes a current collector and a lithium supplement layer and a positive active layer that are sequentially loaded onto a surface of the current collector, and constituents of the lithium supplement layer include a positive electrode lithium supplement material, a solid electrolyte material, and a conductive agent.

Abstract: A service steering method, an apparatus, and a system are provided. The system includes a cloud management platform node, a plurality of resource pools, and a steering node. Each resource pool includes a plurality of hosts configured to run services. An instance cluster corresponding to a target service of a target tenant is created in each resource pool, and an instance in the instance cluster is used to run the target service. In the method, the cloud management platform node obtains available resource information of each resource pool associated with the target service. The cloud management platform node generates, based on the available resource information of each resource pool, a steering plan of the target service. The steering node allocates service traffic of the target service in the time period to the plurality of resource pools according to the steering plan.

Abstract: A method for providing an edge service includes a first security agent that performs data stream transmission with a plurality of endpoint computing devices through an edge link, and each endpoint computing device obtains, using the first security agent, an edge service provided by an edge server. The first security agent determines, based on a data stream transmitted between each endpoint computing device and the first security agent, an abnormal endpoint computing device in the plurality of endpoint computing devices. The first security agent generates link abnormality information, and the link abnormality information includes an address of the abnormal endpoint computing device. The first security agent sends the link abnormality information to the security controller. The security controller receives the link abnormality information, and generates edge link information including an address of a second security agent based on the link abnormality information.

Abstract: A pod deployment method is provided, to increase container deployment flexibility, and meet an access requirement of a user, including: A scheduler obtains requirement information and a pod annotation from an application management cluster. The requirement information includes a time period and a user quantity corresponding to the time period, and the pod annotation indicates a user quantity supported by a single pod. The scheduler determines, based on the user quantity corresponding to the time period and the user quantity supported by the single pod, a quantity M of pods that need to be deployed in the time period, and determines a target node used to deploy the M pods. The scheduler generates a deployment plan based on the quantity M of pods that need to be deployed in the time period and the target node used to deploy the M pods.

Abstract: The present disclosure relates to instance deployment methods, apparatuses, cloud systems, computing devices, and storage mediums. One example method includes obtaining a resource requirement of a service, and deploying instances of the service across a central cloud and an edge cloud based on the resource requirement of the service. In the example method, global resources of the central cloud and the edge cloud are scheduled in combination.

Abstract: A resource scheduling apparatus receives a first resource reservation request and a second resource reservation request, and reserves a first elastic resource for a first tenant and a second resource for a second tenant in a cloud infrastructure. The resource scheduling apparatus receives a resource allocation request requesting to allocate a resource to the first tenant, and when determining that a quantity of resources requested by the resource allocation request is greater than a quantity of first elastic resources reserved by the resource scheduling apparatus for the first tenant in advance, allocates a resource to the first tenant from the second elastic resource.

Abstract: In a streaming media transmission method, a streaming media server receives a first message sent by a terminal device. The first message includes a streaming media identifier, and the first message is used to request streaming media corresponding to the streaming media identifier. The streaming media server processes the streaming media based on scalable video coding SVC to obtain at least one data stream. The streaming media server determines quality of service QoS for each data stream. The streaming media server sends a second message to a first edge streaming media server. The second message includes the QoS of each of the at least one data stream, and the second message indicates the first edge streaming media server to transmit the at least one data stream to the terminal device by using a resource corresponding to the QoS of each data stream.

Abstract: A method may include obtaining an image of a scene from a first perspective, the image including an object, and detecting the object in the image using a machine learning process, where the object may be representative of a known shape with at least four vertices at a first set of points. The method may also include automatically predicting a second set of points corresponding to the at least four vertices of the object in a second perspective of the scene based on the known shape of the object. The method may additionally include constructing, without user input, a transformation matrix to transform a given image from the first perspective to the second perspective based on the first set of points and the second set of points.

Abstract: A method for processing a cloud service in a cloud system, wherein the cloud system includes a hosted cloud, an allied cloud, and an alliance management apparatus, wherein the hosted cloud includes a first region, the allied cloud includes a second region, the hosted cloud further provides a virtual region for a user, and the virtual region is mapped by the second region on the hosted cloud. The alliance management apparatus enables a user of the hosted cloud to obtain unified experience when the user accesses the first region and the virtual region in the cloud system, for example, unified experience in control interface access, and unified experience in application programming interface (API) access.

Abstract: A method may include obtaining a first image of a first view. The first illustrated by the first image may be defined by one or more viewing parameters. The method may also include acquiring, from a machine learning system, a location of an object within the first image, calculating a relative position of the object within the first image using the location of the object within the first image, and determining whether the relative position of the object satisfies a condition. In response to the relative position of the object not satisfying the condition, at least one of the viewing parameters may be adjusted based on the relative position of the object and the condition. The method may further include obtaining a second image of a second view. The second view illustrated by the second image may be defined by the adjusted at least one of the viewing parameters.

Abstract: A client obtains resource requirement information of an application APP running on a terminal device, where the resource requirement information is used to indicate a resource requirement of the APP; the client sends the resource requirement information to a cloud resource scheduling system; the client receives resource allocation information from the cloud resource scheduling system, where the resource allocation information includes information about at least one compute instance allocated by the cloud resource scheduling system based on the resource requirement information, and the at least one compute instance is deployed on at least one far-side site managed by the cloud resource scheduling system; and during running of the APP, the client sends some processing tasks of the APP to the at least one compute instance, and receives results of processing the some processing tasks of the APP by the at least one compute instance.

Abstract: Systems and methods including identifying a physical intelligent moving object (IMO), the physical IMO associated with a current location and a service request; creating, from a digital representation template that is based on the service request, a digital representation instance of the physical IMO in a particular edge cloud of a distributed cloud computing environment that is closest to the current location of the physical IMO; in response to creating the digital representation instance, establishing a network connection between the digital representation and the physical IMO using the particular edge cloud; detecting a movement of the physical IMO from coverage of the particular edge cloud; in response to detecting the movement of the physical IMO: identifying a target edge cloud of the distributed cloud computing environment for coverage of the physical IMO; and establishing a network connection between the digital representation and the physical IMO using the target edge cloud.

Abstract: Systems and methods for identifying physical intelligent moving objects (IMOs); creating, for a first subset of the physical IMOs, digital representations in edge clouds of a distributed cloud computing environment, each digital representation representative of a physical IMO of the first subset of the physical IMOs; generating a cooperative network computing (CNC) platform that includes the digital representations of the first subset of the physical IMOs, each digital representation functioning as a CNC entity in the CNC platform for the corresponding physical IMO; identifying a CNC task of a first physical IMO of the plurality of physical IMOs; and performing, by a first digital representation of the representations that corresponds to the first physical IMO, the CNC task in the CNC platform, including accessing data associated with a second digital representation of the digital representations in the CNC platform to perform the CNC task at the first digital representation.

Abstract: A method may include obtaining an image of a scene from a first perspective, the image including an object, and detecting the object in the image using a machine learning process, where the object may be representative of a known shape with at least four vertices at a first set of points. The method may also include automatically predicting a second set of points corresponding to the at least four vertices of the object in a second perspective of the scene based on the known shape of the object. The method may additionally include constructing, without user input, a transformation matrix to transform a given image from the first perspective to the second perspective based on the first set of points and the second set of points.

Abstract: A method for processing a cloud service in a cloud system, wherein the cloud system includes a hosted cloud, an allied cloud, and an alliance management apparatus, wherein the hosted cloud includes a first region, the allied cloud includes a second region, the hosted cloud further provides a virtual region for a user, and the virtual region is mapped by the second region on the hosted cloud. The alliance management apparatus enables a user of the hosted cloud to obtain unified experience when the user accesses the first region and the virtual region in the cloud system, for example, unified experience in control interface access, and unified experience in application programming interface (API) access.

Abstract: In an example application instance deployment method, a global management platform receives quality of service (QoS) requirement information from a first client. The QoS requirement information includes a first delay requirement, a second delay requirement, and a first quantity of connections. The second delay requirement is better than the first delay requirement, and the QoS requirement information is entered by a first user to the first client. The global management platform selects a first available site that meets the first delay requirement from managed sites. The global management platform deploys one or more first application instances on the first available site. A quantity of connections that can be established to the first application instance is less than the first quantity of connections.

Abstract: Systems and methods for provisioning real-time three-dimensional maps, including: at a first processing stage: updating a three-dimensional map of an environment based on received point cloud data; extracting three-dimensional proposals of objects of the environment; projecting the three-dimensional proposals onto a two-dimensional image of the environment; generating two-dimensional proposals of the objects of the environment; at a second processing stage: detecting the objects of the environment from the two-dimensional image of the environment; generating two-dimensional bounding boxes of the objects of the environment; matching the two-dimensional bounding box with a particular two-dimensional proposal of the two-dimensional proposals; and labeling, for each two-dimensional proposal that is matched to a two-dimensional bounding box, the three-dimensional proposal corresponding to the two-dimensional proposal with semantic information associated with the two-dimensional bounding box that is matched to the

Abstract: Disclosed methods for network modernization include obtaining a list of end-to-end circuits carried in a circuit-switched network, calculating, for each circuit, an early retirement credit (ERC) score and a circuit load factor (CLF) score, selecting, dependent on the ERC and CLF scores, a circuit to migrate to a new network, adding the selected circuit to a circuit migration sequence, and removing the circuit from the list. The ERC score represents the number of circuit-switching units on which no circuits would be carried and that would remain in the network following its removal. The CLF score represents an average number of circuits that would be carried on each circuit-switching unit currently traversed by the circuit following its removal. When two circuits have the highest ERC score, the circuit with the lowest CLF score is selected for migration. The method is repeated until the list is empty.