15 results about "Web cluster" patented technology

The present disclosure relates to computer-implemented methods, software, and systems for distributing task execution at nodes of a web service cluster. A request is sent from an interface gateway to execute a task at a first service node at a web service cluster. The first service node is one of a plurality of service nodes at the web service cluster. The first service node is selected to execute the task based on evaluating a resource status matrix defining resource statuses of the plurality of service nodes in the web service cluster and resource requirements of the task. A status response from the first service node is obtained after executing the task. The status response comprises a current resource snapshot of the first service node. The resource status matrix is updated based on the obtained status response from the first service node.

The embodiment of the invention discloses a terminal data management method and device, electronic equipment and a storage medium. Through a multi-terminal cluster management triggering mechanism, monitoring data, installation position data and equipment operation state data are classified, only core data are transmitted and matched with terminal identifiers, the transmission efficiency is improved, and redundant data occupation is reduced; secondly, a standby switching design of a main communication module and a virtual operator network cluster communication module is adopted, so that the problem of data interruption caused by link instability in a complex environment is solved; and meanwhile, the core data are uniformly reported to the multi-terminal management server, so that multi-terminal uniform management and control and real-time state control are realized, the problems of low reliability and poor management efficiency of the existing scheme are solved, and the reliability and management efficiency of terminal data management are improved.

Techniques are disclosed for a computing system comprising processing circuitry having access to a storage device, the processing circuitry configured to: generate, by a network controller executing in a software defined network (SDN), an advertisement in a first network cluster executing within a container orchestration platform of the SDN, wherein the advertisement conforms to a routing protocol and comprises information identifying a network service executing in the first network cluster, wherein the network service exposes a backend of a network application to the container orchestration platform of the SDN; and broadcast, by the network controller and to a second network cluster executing within the container orchestration platform of the SDN, the advertisement in accordance with the routing protocol.

The embodiments of the present disclosure provide a network cluster data acquisition method and apparatus, the network cluster data acquisition method comprising: acquiring question text, the question text being for at least one network cluster; processing the question text on the basis of a target data processing model to generate response text, wherein the response text comprises answer text and step analysis text, the answer text is generated by the target data processing model on the basis of the step analysis text, and the step analysis text is generated by the target data processing model according to the question text and network cluster data corresponding to each network cluster. By using a target data processing model for data acquisition and processing, the present solution addresses the operational complexity and low efficiency of traditional methods, thereby achieving flexible multi-dimensional, multi-indicator responsiveness, and significantly improving the speed and accuracy of data acquisition.

The invention discloses a private network cluster access system and method based on gRPC bidirectional authentication. The flexibility, the expandability and the operation and maintenance efficiency of the system are remarkably improved while the safety is guaranteed. The system comprises a container management module used for realizing container management services including receiving and processing of user requests, business arrangement and request forwarding, and user interface management; the cluster end proxy module is used for realizing active connection and registration, long connection maintenance, security instruction execution, HTTP request proxy, local authority control, log auditing and self-healing capability; and the agent management module is used for realizing cluster connection management, multi-cluster access mode switching, access strategy management, cluster end agent centralized management, state monitoring and alarming, instruction issuing and routing, security flow agent, authority control and authentication and whole-process encryption guarantee.

The present disclosure relates to computer-implemented methods, software, and systems for distributing task execution at nodes of a web service cluster. A request is sent from an interface gateway to execute a task at a first service node at a web service cluster. The first service node is one of a plurality of service nodes at the web service cluster. The first service node is selected to execute the task based on evaluating a resource status matrix defining resource statuses of the plurality of service nodes in the web service cluster and resource requirements of the task. A status response from the first service node is obtained after executing the task. The status response comprises a current resource snapshot of the first service node. The resource status matrix is updated based on the obtained status response from the first service node.

The invention discloses a flow control device and method based on a request preprocessing mechanism. The device comprises a routing analysis module used for receiving a target request and analyzing the target request to obtain a routing result; wherein the routing result is used for representing a forwarding path of the target request; and the network cluster module is connected with the route analysis module, and is used for matching from a preset route set according to the route result to obtain a target route, and forwarding the target request to a target address through the target route. According to the technical scheme, refined flow control and efficient scheduling of client requests in a complex service scene or network environment can be realized.

This application discloses a cloud-native network resource allocation method, apparatus, device, and storage medium, relating to the field of resource management technology, and applied to a network twin service for cloud-native network clusters. The method includes: constructing a topology graph representing the dependencies between microservices using instance information of each microservice in the cloud-native network cluster; evaluating the resource usage of each microservice based on preset business experience service metrics and real-time user experience data, combined with the topology graph; the preset business experience service metrics are performance indicators generated based on historical user experience data of each microservice, and the real-time user experience data are user experience data of each microservice in real time; and performing resource allocation operations on each microservice according to the corresponding evaluation results. This combines network twin technology with cloud-native network clusters, allocating resources to each microservice with a user experience-oriented approach, which can accurately optimize resource allocation to improve user experience and resource utilization efficiency.

The invention provides an RU information acquisition method and a container scheduling method and device based on RU information. The RU information acquisition method comprises the steps that first equipment acquires information of RU equipment managed by schedulable nodes in a network cluster; and the first device sends the information of the RU device to a second device, so as to determine a target scheduling node in the schedulable nodes. Therefore, the capability of the existing second equipment is enhanced, the collection of the RU information is supplemented, and the node selection is performed on the to-be-scheduled container based on the collected RU information of the schedulable node, so that the condition that the to-be-scheduled container, especially the to-be-scheduled container of a high-real-time task, is scheduled to the node which does not meet the RU requirement of the to-be-scheduled container is avoided, and the scheduling efficiency of the to-be-scheduled container is improved. Compared with the prior art, the method can be better applied to container scheduling in a high-real-time RU demand scene, not only can proper nodes be more accurately selected for container scheduling, but also the overall reliability of the network, the user experience and the resource utilization efficiency can be improved.

The invention relates to the technical field of automobile electronic network management, in particular to a partial network cluster fault isolation and recovery mechanism conforming to the AUTOSAR standard. Through PNC fault detection, isolation and self-healing recovery steps, PNC-level fault rapid detection, intelligent isolation and automatic recovery are realized. In the detection stage, the state is monitored through an NM-PDU containing a PNI / PNSR bit, and a fault is judged in a multi-condition mode; in the isolation stage, a fault PNC is marked by a coordination node, an isolation message is sent by adopting a time-sharing grading strategy, and a process is synchronously closed; in the recovery stage, instantaneous, continuous and hardware-level faults are adapted respectively through a three-level progressive mechanism. According to the method, the limitation of traditional whole network management is broken through, PNC-level accurate fault control is realized, fault diffusion is avoided, the recovery efficiency and robustness are improved, the AUTOSAR standard is followed, parameters can be flexibly configured, multiple types of vehicle network scenes are adapted, and stable operation of the system is guaranteed.

The application provides a kind of computing power network cluster dynamic construction method based on constraint condition, the method includes: the information of registered computing power node and extract the computing task characteristics in task demand;According to the computing task characteristics, the ability of computing power node is evaluated, wherein the ability evaluation is obtained according to the characteristic function of computing task;The computing power node as task initiation node is as root node, and the next computing power node is evaluated and selected based on the reachable routing path of routing table and the constraint condition, wherein the evaluation value of branch computing power node is obtained by evaluation function according to the ability of evaluation node through the ability evaluation Vi;The evaluation value search fitting of branch computing power node that can be routed is carried out by level by level, and the best computing power multistage collaborative computing power node cluster for the computing task is obtained.The application also provides a kind of computing power network cluster dynamic construction system based on constraint condition.

A method includes executing replicated virtual machines on multiple computing nodes within a cluster in a distributed computing network. Each virtual machine includes an application, and the application includes bytecode. The method also includes sequentially recording, in a replicated transaction log, transactions of the virtual machines and one or more upgrades to the bytecode. The method further includes generating, for each computing node, one or more snapshots and truncating the transaction log. Each snapshot captures an application state and the bytecode of the associated virtual machine. In addition, the method includes, in response to restoration of at least one computing node in the cluster or an addition of at least one additional computing node to the cluster, restoring one or more virtual machines to a current application state of one or more other virtual machines using the transaction log and at least one of the one or more snapshots.

The present disclosure relates to a method and a system for automatically upgrading a target network cluster The method comprises accessing, by a processing unit [302], a first container from a first set of containers associated with one or more network clusters. The method comprises initiating an upgrade procedure associated with the first container. The method comprises generating, by a generation unit [304], a modified set of container data in the first container based on the initiated upgrade procedure. The method comprises duplicating at the first container, the modified set of container data from the first container. The method comprises identifying, by an identification unit [306], the target network cluster from the one or more network clusters based on the upgrade procedure. The method comprises automatically upgrading the target network cluster by applying the duplicated modified container data to a second set of containers associated with the target network cluster.

Provided are system, method, and device for automatically perform node cluster failure recovery. According to example embodiments, the system may be configured to: receive, from a Service Management and Orchestration (SMO), a network function (NF) deployment request to deploy a network function (NF); in response to receiving the NF deployment request, deploy the NF at a first node cluster; detect a failure at the first node cluster; transmit, to the SMO, a notification including information related to the detected failure at the first node cluster; receive, from the SMO, a network function (NF) re-deployment request to re-deploy the NF; and in response to receiving the NF re-deployment request and in response to detecting the failure at the first node cluster, re-deploy the NF at a second node cluster.