Abstract: A computing node management system includes a central offload node and a plurality of computing nodes. A riser card is inserted into each computing node. Each riser card is connected to the central offload node. Each riser card establishes a communication channel between the central offload node and the computing node into which the riser card is inserted. The central offload node is configured to provide resources for the plurality of computing nodes through the communication channels.

Abstract: When target data is to be accessed, a computing node determines whether the target data meets a computing pushdown condition. When the target data meets the computing pushdown condition, send a data access request to a storage node. After receiving the data access request, the storage node obtains a dataset to which the target data belongs, performs the computing service based on the computing indication, accesses the target data in the dataset, and feeds back an access result to the computing node.

Abstract: A storage node configuration method includes obtaining service requirement information, where the service requirement information includes a burst access moment, a burst access bandwidth, or burst access duration of a computing node for a storage node; determining target configuration information based on the service requirement information, where the target configuration information is used to configure a peak bandwidth and peak bandwidth duration that are of the storage node; and sending the target configuration information to the storage node.

Abstract: This application provides a resource configuration prediction method and device applied to resource configuration prediction of a cloud service system, to improve resource allocation efficiency and reduce costs of the cloud service system. The method in embodiments of this application includes: obtaining original data of a cloud service system, where the original data includes running data of the cloud service system in a production environment or a production-like environment; obtaining training data based on the original data, and performing deep learning based on the training data to construct and obtain a resource configuration prediction model; when resource demand data input by a user is obtained, generating input data of the resource configuration prediction model based on the resource demand data; and performing prediction based on the input data and the resource configuration prediction model, to obtain resource configuration data of the cloud service system.

Abstract: Systems and methods embedding a frequency domain-based watermark in tabular data are provided. In some methods, a covariance matrix is computed for the tabular data. Row vectors of the tabular data are projected onto two orthonormal vectors e1, e2 of the covariance matrix. A signal space for the projected row vectors is determined. A frequency f* with a lowest power from the signal space is located. A watermark is embedded in the tabular data based on the frequency f*.

Abstract: A front-end device is configured to capture a light point image of an authenticatee and send the light point image to the back-end device, where the light point image is an image captured from the authenticatee under irradiation of multi-light points, and the light point image includes a face of the authenticatee. The back-end device of the authentication system is configured to perform face anti-spoofing detection on the authenticatee based on the received light point image to obtain an authentication result.

Abstract: This application relates to the field of data processing technologies, and in particular, to a node for running a container group, and a container group management system and method. A connection control module, a sidecar cluster, and a first service container group are run on the node. The sidecar cluster includes at least two sidecars. The connection control module is configured to: receive a sidecar allocation policy sent by a console connected to the node, select a first sidecar from the sidecar cluster according to the sidecar allocation policy, and forward, to the first sidecar, a data packet sent by the first service container group. The first sidecar is configured to perform traffic management on the data packet sent by the first service container group. In this application, a sidecar may be selected for a service container group, to perform better traffic management on the service container group.

Abstract: This application discloses a resource provisioning method and apparatus, and a computing device cluster, relating to the field of cloud computing technologies. The method includes: obtaining a first resource request, where the first resource request includes a first resource type of a first virtual resource that is requested to be provisioned; determining, in a plurality of physical resources, at least one physical resource on which a virtual resource of the first resource type is deployed; obtaining a provisioning priority of the at least one physical resource for the virtual resource of the first resource type, where the provisioning priority indicates a provisioning sequence of the at least one physical resource for the virtual resource of the first resource type; and controlling, based on the provisioning priority, the at least one physical resource to provision the first virtual resource.

Abstract: Code inputted by a user is received through a user interface. A feature of context of to-be-completed code is determined based on the code inputted by the user. Next, at least one candidate item of the to-be-completed code is determined from a context database based on the feature of context of the to-be-completed code. The context database stores sample code and a feature of context of the sample code. Then, the at least one candidate item is presented to the user through the user interface.

Abstract: This disclosure provides an object download method. The method includes: A first client sends a first seed obtaining request to an object storage service on a first server; the first server sends a seed file of a target object to the first client based on the first seed obtaining request; the first client sends a first multi-source download provider obtaining request to a second server based on the seed file, where the first multi-source download provider obtaining request is used to request to provide a list of at least one client that downloads the target object; the second server sends a first multi-source download provider list to the first client based on the first multi-source download provider obtaining request; and the first client receives a first part of the target object from the first server, and receives a second part of the target object from the second client.

Abstract: A data access control method includes configuring authentication information for data access control, where the authentication information includes one or more of an identity clearance level, a resource security level, or an environment security level, the identity clearance level indicates access permission of a user, the resource security level indicates a security level of a cloud resource, and the environment security level indicates a security level of a cloud environment; receiving a data access request from the user; and processing the data access request according to authentication logic, where the authentication logic is for controlling data access permission of the user in the cloud resource or the cloud environment, or is for controlling a flow direction of the data between cloud resources or cloud environments, and the flow direction is from a low security level to a high security level.

Abstract: A video processing apparatus and a cloud application are disposed in a data center. A communication connection is established between the data center and a terminal device. The method includes: performing rendering to generate raw video data, where the raw video data is obtained by rendering raw model data corresponding to the raw video data, the raw model data is generated by the cloud application according to an application operation instruction sent by the terminal device through the communication connection, and the raw video data includes M rendering layers; and encoding the M rendering layers in the raw video data into i encoding layers, to generate a target video bitstream, where a first part of rendering layers in the M rendering layers is encoded into a first encoding layer, . . . , and an ith part of rendering layers in the M rendering layers is encoded into an ith encoding layer; M?i?1.

Abstract: This disclosure discloses a garbage collection method and apparatus. In the method, a client of a distributed storage system may send a garbage collection request to a server of the distributed storage system. The server may determine, based on the garbage collection request, that a target unit data block is a valid data block, and send a move command to a storage device storing the target unit data block, to cause the storage device to move the target unit data block from a first storage position to a second storage position. As a server moves data between storage positions of storage devices accessed by the server, instead of a client moving data between servers, network bandwidth between the client and the server is reduced.

Abstract: This application relates to the field of cloud computing technologies and provides a cloud disk service change recommendation method and apparatus. An example method is applied to a cloud management platform configured to manage infrastructure that provides a cloud service. The infrastructure includes a storage pool including storage space of a plurality of cloud servers. The storage pool is virtually divided into a plurality of cloud disks. The method includes obtaining running data of a first cloud disk in the plurality of cloud disks. The running data of the first cloud disk includes a flow control parameter and capacity usage. The method further includes determining a change policy for the first cloud disk based on the running data of the first cloud disk and providing a recommendation interface for a tenant of the first cloud disk. The recommendation interface includes the change policy.

Abstract: A method includes: receiving downsampling policies at a plurality of levels that are defined by a user, where a downsampling policy at each level indicates to downsample data in the plurality of time shards based on a defined sampling time interval after raw data is stored in a plurality of time shards and a defined time interval has elapsed; determining a first downsampling policy in the downsampling policies at the plurality of levels based on a first time interval that has elapsed after first raw data is stored in a first time shard, where the longer first time interval indicates a longer sampling time interval of the first downsampling policy; obtaining first data currently stored in the first time shard; and downsampling the first data according to the first downsampling policy, to obtain second data.

Abstract: A method comprises: obtaining, by a source terminal, first media data of a real person and comprising a first video or a first audio of the real person; extracting, by the source terminal and from the first media data, feature data for describing the real person and comprising a facial feature, a morphological feature, or a voice feature of the real person, wherein the facial feature and the morphological feature are from the first video, and wherein the voice feature is from the first audio; sending, by the source terminal and to a cloud server, the feature data; generating, by the cloud server and based on the feature data and a multimodal AI-driven model, second media data of a digital human and comprising a second video or a second audio of the digital human; and sending, by the cloud server and to a destination terminal, the second media data.

Abstract: A cloud service-based data forwarding method applying to a cloud platform, the cloud platform includes a first cloud system, the first cloud system includes a cloud computing device and a forwarding device, and the first cloud system and an extranet system are connected to each other through an extranet gateway. The method includes: The forwarding device obtains a target packet sent by the cloud computing device to the extranet system, and obtains a forwarding attribute of the cloud computing device. When the forwarding attribute of the cloud computing device is a first attribute, the forwarding device forwards the target packet by using the extranet gateway as a next hop of the target packet. When the forwarding attribute of the cloud computing device is a second attribute, the forwarding device forwards the target packet to the extranet gateway based on a routing relationship of the forwarding device.

Abstract: An SQL defense method includes an SQL defense rule configuration unit configuring corresponding SQL defense rules for a plurality of SQL engines through a unified entrance, where each SQL defense rule includes an effective SQL engine and a corresponding execution action, and the SQL defense rules corresponding to the plurality of SQL engines have a same format; and the SQL defense rule configuration unit sending each configured SQL defense rule to a corresponding SQL engine based on the effective SQL engine in each SQL defense rule, so that the SQL engine performs defense on a received SQL statement according to the obtained SQL defense rule.

Abstract: A cloud system resource set recommendation method includes after receiving a cloud system resource set recommendation request triggered by a user and used to request to obtain cloud system resource sets that can support a target cloud service and meet a performance requirement, the recommendation apparatus selects, based on the cloud system resource set recommendation request, from the existing cloud system resource sets supporting the target cloud service, a target cloud system resource set meeting the performance requirement, and feeds back the target cloud system resource set to the user. The user does not need to select a required cloud system resource set from several limited resource sets with fixed configurations, but only needs to notify the recommendation apparatus of a target cloud service and a performance requirement that need to be supported by the cloud system resource set.

Abstract: A validation method includes that a plurality of decentralized application nodes aggregate, using a key aggregation algorithm, public keys held by the plurality of decentralized application nodes, to generate a public address of a blockchain network, and separately transfer tokens to the public address. The plurality of decentralized application nodes transfers the tokens at the public address to a staking account, generates a plurality of staking private keys, and allocates the plurality of staking private keys to a distributed validator technology (DVT) network. A plurality of DVT nodes signs a transaction using the staking private keys respectively held by the plurality of DVT nodes, to obtain an aggregated signature, and then provides the aggregated signature for a consensus node, to participate in validation of the transaction.