164 results about "Service grid" patented technology

The embodiment of the invention provides a multi-agent arrangement method and device based on a service grid and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: generating corresponding domain-specific language description information based on workflow information of a plurality of agents; and based on the domain-specific language description information, generating routing information corresponding to the workflow information of the plurality of agents, the routing information being used for defining a calling relationship of the plurality of agents in the service grid. According to the scheme, the labor cost is reduced, and the arrangement efficiency is improved.

The invention relates to the technical field of urban planning, in particular to a charging facility planning method and device based on machine learning and a medium, and the method comprises the following steps: carrying out the grid division of a planning region, which comprises a plurality of charging service grids; carrying out vehicle ownership prediction and charging facility demand prediction to obtain a charging demand quantity; adopting a clustering algorithm to obtain a plurality of charging block areas with balanced charging demand quantity; and constructing an objective function by maximizing the station revenue of each charging block area, constructing constraint conditions by full coverage of charging demands, calling an aurora optimization algorithm to obtain an optimal planning scheme of the charging facilities, and completing the planning of the charging facilities by adopting the optimal planning scheme of the charging facilities. According to the method, the problems of incomplete analysis and unreasonable partition optimization in the prior art can be solved, and various influence factors of charging facility layout are comprehensively considered, so that efficient layout and resource allocation of a charging network are realized, and the overall quality of charging services and the user satisfaction are remarkably improved.

The invention discloses an intelligent gray scale and multi-version adaptive deployment system and method based on Kubernetes, and the method comprises the steps: through an Operator which operates in Kubernetes and integrates a plurality of intelligent modules, the Operator can collect and fuse a business performance index and a technical performance index, and generates a comprehensive "version health score"; according to the real-time state of the health score, the rhythm and the step length of flow release are adaptively adjusted; meanwhile, through integration with a service grid, staged and context-based accurate flow routing is realized; when serious problems occur, safe rollback can be automatically executed, and cooling locking can be applied. According to the method, the service target and the release process are deeply bound, so that the intelligence of release decision, the self-adaptive adjustment of the release process and the fine control of the risk are realized, and the security, efficiency and success rate of software release are remarkably improved.

An apparatus and system of providing a service registry function (SRF) and service discovery in a 6G system are described. Registration procedures are provided for a service instance on a user equipment (UE) or in the 6G system to register to the SRF with or without a service mesh. The SRF provides a list of service instances based on criteria in service discovery inquiries from the UE or network function (NF) and notifies a subscribed party about a status change of a particular service instance. The service discovery enables the UE to discover a computing service instance in the 6G system by control plane service discovery to find a service orchestration and chaining function (SOCF) and user plane service discovery to find the computing service instance.

The invention discloses a dynamic current limiting method, device and equipment, a medium and a product. The dynamic flow limiting method comprises the steps of performing flow prediction and system load prediction based on historical service operation data to obtain a predicted flow value, a predicted load score and a dependency weight of a service in a system; the historical service operation data comprises historical flow data, system resource data and business index data; generating a flow limiting rule according to the predicted flow value, the predicted load score and the dependency weight, and synchronizing the flow limiting rule to a service grid layer; and based on the current service operation data collected by the service grid layer in the service operation process and the current limiting rule, carrying out current limiting on the currently operated service. According to the technical scheme of the embodiment of the invention, the dynamic adjustment of the current limiting rule can be realized.

The invention provides a firewall configuration method for protecting cloud security. The method comprises the following steps: acquiring a firewall policy related to a target computing node in a target computing cluster; wherein the target computing node runs at least one target container group, and the firewall policy comprises a mapping relationship between a network traffic filtering policy and the container group applying the network traffic filtering policy; determining a network flow filtering strategy corresponding to each target container group according to the firewall strategy; and for each target container group in the target computing node, executing the following steps: configuring a network traffic filtering strategy corresponding to the target container group in a table used for modifying the network traffic content in the target container group. In the method, aiming at each container group, no matter whether the container group is in a service grid mode or not, the firewall function can be realized, the network flow management can be completed, and meanwhile, aiming at the container group in the service grid mode, the subsequent communication agent is not influenced.

A system can execute a containerized application that comprises a microservice in a decentralized service mesh architecture, and a sidecar. The system can intercept, by the containerized application, a call from the microservice that is directed to a remote endpoint, and direct the call to the sidecar. The system can communicate, by the sidecar to an identity manager, service account credentials associated with the microservice, resulting in receiving an identity token associated with the microservice. The system can determine, by the sidecar, connectivity information of the remote endpoint based on a virtual address of the remote endpoint identified in the call. The system can communicate, by the sidecar to a token exchanger, the identity token and the connectivity information, resulting in receiving an access token and a network route to the remote endpoint. The system can relay, by the sidecar, network traffic between the microservice and the remote endpoint.

The invention relates to the technical field of internet risk collaborative management, and discloses an internet data risk early warning and collaborative management system and method.The internet data risk early warning and collaborative management method comprises the steps that a service grid enhanced federal learning unified framework is constructed, and a tetrad structure is formed; a seamless integrated architecture of federal learning and a service grid is realized; developing a federal contribution evaluation mechanism based on a service level agreement; a self-adaptive service discovery and dynamic combination technology is realized, and an optimal service combination scheme is generated; constructing a federated aggregation algorithm of data quality perception, and introducing a quality weight factor optimization model for training; designing a multi-level cache strategy; according to the invention, through the fusion of federal learning and the micro-service architecture, the purpose of keeping efficient collaboration while protecting data privacy is realized, the original data of each participant is always kept locally, only necessary model parameters are shared, and the privacy leakage risk is reduced.

The invention discloses a server gray release method, device and equipment and a medium, and belongs to the technical field of computers. The method comprises the steps that inbound request flow of a client side is intercepted through a side vehicle agent of a data plane of a service grid, feature identification in a request is extracted, a plurality of version instances of a target application and corresponding routing strategy rules are configured in a control plane of the service grid, and the routing strategy rules comprise gray strategies; and based on a matching result of the feature identifier and the routing policy rule, distributing the inbound request traffic of the client to a plurality of version instances corresponding to the target application according to a gray policy in the routing policy rule. According to the method, the routing capability is increased in the Inbound direction of the service grid, so that the inbound request flow of the client is subjected to request redirection after being matched with the routing, the gray release capability is sunk to the infrastructure level, and the standardization and generalization of the gray release function are realized.

The invention discloses a service grid stability guarantee method and device, and belongs to the technical field of computers. The method comprises the following steps: creating a service grid version management rule based on a self-defined resource definition mechanism of Kubernetes, and declaring a binding relationship between a target Deployment and a side vehicle agent version; intercepting a Pod creation request through an access controller of Kubernetes, and triggering an injection process of a service grid control plane; the service grid control plane judges whether the Pod is associated with the target Deployment or not by analyzing the label of the Deployment to which the Pod belongs; and if the Pod is associated with the target Deployment, injecting the side vehicle agent of the corresponding version into the Pod. According to the invention, the version of the service grid can still be finely controlled under the condition that the service and the service grid are deployed and decoupled, so that the service container and the verified service grid version are always deployed together; and the self-healing of the service grid and the capability that the user can autonomously and emergently exit the service grid are realized under the condition of not influencing the service.

The invention provides a game resource scheduling management method and system based on cloud computing, and relates to the technical field of game resources, the method comprises the following steps: decomposing a server of a cloud game application, and establishing a multi-attribute micro-service; constructing a heterogeneous resource pool containing computing nodes of different architecture types; performing request type demand analysis according to the received game session request, and determining a demand micro-service combination; based on the hardware preference features and the performance portraits, configuring computing nodes of the micro-services, and deploying corresponding micro-service instances on the computing nodes; and based on the demand micro-service combination, establishing communication connection between corresponding attribute micro-service instances through a service grid, and generating a collaborative scheduling execution instruction. By means of the method and device, the technical problem that in the prior art, due to the fact that resource scheduling is coarse in granularity and hardware resources are not fully utilized, computing resources are wasted, hardware loads are unbalanced, and the performance of the game server is affected is solved, and the resource utilization rate is increased through fine-granularity task-level scheduling.

The invention relates to the technical field of intelligent parking management, and discloses an intelligent parking management method and system based on license plate recognition, and the method comprises the steps: dividing a micro-service assembly; a service grid control layer is constructed, communication between services is intercepted through a side vehicle agent, and a service quality map is constructed; a dynamic routing decision model is constructed, and a reinforcement learning algorithm is adopted for training according to the service quality map; periodically executing a network topology optimization algorithm; faults are actively injected through a flow mirroring method and a chaos engineering method, potential problems are predicted, and an automatic recovery process is triggered based on an anomaly detection result; according to the invention, by constructing a micro-service architecture, an adaptive network topology and an active disaster recovery mechanism are realized, and the problems of performance bottleneck, insufficient reliability and poor expansibility of a traditional license plate recognition system in large-scale application are effectively solved.

The invention relates to the technical field of computing power distribution, and discloses a data hotspot identification and computing power dynamic reservation scheduling method. According to the method, suspicion hotspot data are monitored and identified in real time, a shadow execution copy with a minimum resource quota is constructed for the suspicion hotspot data, part of real-time flow is mirrored to the shadow copy for pressure testing without influencing a main service, and after a pressure verification score is calculated to confirm a hotspot, shadow copy resources are improved to a high-performance level and directed diversion is carried out; meanwhile, the method comprises the steps of self-adaptive threshold adjustment and hot spot cooling and capacity reduction, interaction is carried out on the basis of a centralized controller, a container arrangement platform and a service grid, hot spots can be accurately recognized, the computing power can be dynamically adapted, the resource utilization rate is increased, and the service stability and the resource efficiency are balanced.

A computer-implemented method performs dynamic filtering within a service mesh. While transferring data from one microservice to another microservice, each microservice evaluates a level of trust with each upstream and downstream microservice based on the level of trust score. Each microservice evaluates which data should be filtered while transferring data to downstream microservice and which data should be filtered while receiving data from upstream microservice.

A system (100) for distributed AI-driven content routing within a cloud-native service network, comprising: a. a traffic data collection module configured to continuously collect and aggregate real-time traffic data from services within the service network, including data related to traffic volume, latency, error rates and service health; b. an AI-based traffic analysis and prediction module that uses machine learning algorithms to process the collected traffic data and predict network conditions, detect anomalies, and predict potential congestion or failure points; c. a dynamic content routing module integrated into the service network, configured to dynamically adjust content routing decisions based on the predictions and insights provided by the AI-based traffic analysis module to optimize resource utilization and minimize latency; d. a fault tolerance and self-healing module that continuously monitors the state of the network and, in the event of a failure or disruption, triggers automatic recovery actions, such as rerouting traffic, providing additional resources or activating backup protocols; e. a distributed AI model integration and management module configured to distribute AI processing across multiple nodes in the service mesh, enabling decentralized decision-making and efficient model updating and synchronization; f. a monitoring and feedback module designed to track system performance, collect real-time feedback on routing decisions and troubleshooting actions, and ensure continuous optimization through an iterative feedback loop; g. the system continuously adapts to changing network conditions, thus ensuring optimal content delivery and fault resistance across the entire service network.

A machine-readable storage medium includes instructions stored thereupon, which when executed by processing circuitry of a computing node operable to implement a service mesh control plane (SMCP) in a MEC network, cause the processing circuitry to decode an attestation request received from a sidecar proxy of a deployable instance. The sidecar proxy is instantiated on a MEC host. Evidence information is collected from the deployable instance responsive to the attestation request, the evidence information comprising at least one security configuration of the deployable instance. An attestation of the evidence information is performed using a verified configuration of the deployable instance to generate an integrity report. An attestation token is generated based on the integrity report and is encoded for transmission to the MEC host. The attestation token authorizes the sidecar proxy to obtain configuration to facilitate a data exchange between the deployable instance and at least another deployable instance.

In one embodiment, service mesh monitoring with infrastructure awareness is provided herein. An example method herein may comprise: parsing, by a service mesh manager process, a package manager chart to learn a plurality of cloud resources associated with a given application; monitoring performance metrics for each individual resource for the plurality of cloud resources; aggregating the performance metrics for each individual resource into an overall performance level of the given application; and generating a holistic health report of the overall performance level of the given application that is further indicative of the performance metrics for each individual resource.

An apparatus to facilitate metrics and security-based accelerator service rescheduling and auto-scaling using a programmable network device is disclosed. The apparatus includes processors to collect metrics corresponding to communication links between microservices of a service managed by a service mesh; determine, based on analysis of the metrics, that a workload of the service can be accelerated by offload to a hardware accelerator device; generate a scaling request to cause the hardware accelerator device to be allocated to a cluster of hardware devices configured for the service; cause the scaling request to be transmitted to a programmable network device managing the hardware accelerator device, the programmable network device to allocate the hardware accelerator device to the cluster and to register the hardware accelerator device with the service mesh; and schedule the workload of the service to the hardware accelerator device.

Various methods, systems, and use cases for securely managing, generating, and controlling access to keys in a service mesh are discussed herein. In various examples, key protection operations include service mesh signing key protection and service mesh communication key protection, for a secure transport session between services such as conducted with mutual transport layer security (mTLS). For instance, such key protection operations may be used to establish communications between the service host and another entity within the service mesh, in a secure transport session, based on use of a private key (secured using a confidential computing technology) in a secure enclave or other secure compute environment to sign one or more keys for the secure transport session.

The invention relates to the cross technical field of artificial intelligence operation and maintenance and cloud native computing, in particular to an AI model full life cycle management method and system based on cloud native, and the method comprises the steps: constructing a containerized interactive development environment, and generating an invariable OCI mirror image containing a model code and a dependency library; a versioning model registration center of a three-layer storage architecture is adopted, and strong consistency management of model files and metadata is achieved; an automatic CI / CD assembly line is driven through a declarative workflow engine, and event triggering and timing scheduling are supported; the reasoning service deployment module based on the service grid realizes gray release and elastic capacity expansion and contraction; a multi-dimensional monitoring index system is established, and model performance attenuation and data drift are detected in real time; and designing an adaptive resource scheduling algorithm, and dynamically optimizing heterogeneous computing resource allocation. The resources can be dynamically adjusted according to the actual load, and high performance and high availability of the model service are ensured.

The embodiment of the invention provides a model servitization method and system and a storage medium. A grid agent and a plurality of model service instances are deployed for model service in a service grid to support multi-model dynamic loading; on the basis, the grid agent can route the received model calling request to one of the target service instances, and the target service instance senses which model is required by the model calling request, and the target service instance can execute continuous routing on the model calling request, so that the model calling request can be continuously routed. Therefore, the model calling request is routed to the model service instance capable of providing the required model. Therefore, even if the grid agent provided in the service gateway does not correctly hit the model service instance, re-routing is not needed, but the model calling request can be routed to the correct model service instance based on the continuous routing capability designed for the model service instance in the embodiment, so that the routing hit rate can be effectively improved, and the routing efficiency is improved.

The invention provides a traffic management method, a service grid system, a device and a cluster. The method comprises the following steps: a first L4 agent receives a first data packet sent by a first service container group; the first L4 agent sends the identifier of the first tenant and the first data packet to a first L7 agent; the first L7 agent identifies configuration information of a first service in configuration information of multiple services based on the identifier of the first tenant, and the first service belongs to the first tenant; and the first L7 agent performs flow management on the first data packet based on the configuration information of the first service. According to the method, the computing resource utilization rate of the service grid system can be improved.

The invention discloses a traffic request scheduling method, electronic equipment and a storage medium. The method comprises the following steps: acquiring a user request corresponding to at least one workload; the service grid agent corresponding to the at least one workload is used for intercepting and analyzing the user request to obtain request metadata, and the request metadata is used for describing request attributes of the user request; scheduling decision making is carried out according to the request metadata, a scheduling decision result corresponding to the user request is obtained, and the scheduling decision result is used for determining a request processing mode corresponding to the user request; and in response to determining to accept the user request according to the scheduling decision result, forwarding the user request to the target server. According to the method and the device, the technical problems of low scheduling efficiency and poor flexibility when high-concurrency traffic requests are processed in a service grid in the prior art are solved.

The present application relates to the technical field of distributed system testing and operation and maintenance monitoring, and particularly relates to a server test environment automatic monitoring method and system, comprising: collecting a micro-service cluster directed call topology structure, constructing a distributed cell layer with node and edge vector spaces; parsing data serialization rules to extract constraint restriction mapping matrices, assembling a Hodge-Dirac operator; obtaining real-time business state vector groups to splice into zero-order co-chains, using the operator to solve an algebraic Laplace matrix to calculate a quadratic form energy functional; when determining that the quadratic form energy functional is greater than zero, generating an early warning signal, constructing and solving a discrete layer diffusion dynamics equation based on the algebraic Laplace matrix, obtaining a state compensation update matrix; finally, analyzing the state compensation update matrix into reverse rollback and cache erasure instructions, and submitting the instructions to a service mesh data plane proxy component for execution. The present application realizes code-free cross-node semantic conflict quantification and distributed consistency state self-healing closed loop.

This invention discloses a service mesh user status verification method, apparatus, device, and medium. The method includes: acquiring an access request sent by a client; wherein the access request carries a predefined level token; parsing the predefined level token to obtain predefined level token information; determining whether the predefined level token information meets the access requirements; and if the predefined level token information meets the access requirements, allowing access to the service mesh. This technical solution, by classifying tokens into different levels, can reduce the parsing time on the service mesh side and improve throughput.

The invention provides a data processing method and device for a service grid platform and electronic equipment, and the service grid platform is connected with a registration center through a first controller, and obtains specified service data; based on a target service selected by a user and included in the service grid platform, generating service configuration data corresponding to the target service, issuing the service configuration data to k8s, and storing the service configuration data; the first controller monitors change of service configuration data in k8s through the k8s API-Server, obtains a registration center service model of a target service based on the service configuration data stored in the k8s, and converts the service grid platform service model into an agent service model to be issued to all agents; according to the method, the registration center is automatically docked, so that the service information and the instance information of the registration center can be synchronously displayed on the service grid platform, real-time synchronization of the grid platform and the service grid platform is realized, and manual participation is not needed.

According to one embodiment, a method, computer system, and computer program product for gamification of a cloud application is provided. The present invention may include intercepting communications between an end device and the application using a service mesh; analyzing frontend code of the application and external and internal network traffic of the application to produce a plurality of analyzed data; identifying an application-agnostic gamification strategy based on one or more user interface (UI) elements in the application; identifying an application-aware gamification strategy based on the plurality of analyzed data; and dynamically injecting gamification elements from the identified gamification strategies into the frontend code of the application.

This invention provides a technique for microservice scheduling and improving data read / write efficiency based on runtime state analysis. Microservice systems have become one of the fundamental technology platforms for building large-scale, complex applications. By encapsulating modular information processing units in a lightweight virtual machine manner, high fault tolerance is achieved. Various application functions are orchestrated through service mesh to realize stable large-scale, complex application systems. Microservices often need to access multiple data sources or data lake systems, and the location of data has a critical impact on data read / write efficiency. For example, in a distributed storage system, having the execution node of a microservice coincide with or be close to the data storage node will greatly improve data processing efficiency. Addressing the efficiency problem of microservices in data-intensive scenarios in large-scale applications, this invention proposes a technique based on analyzing the runtime state of data read / write operations to determine the optimal location for microservice scheduling and execution. By shortening the data path, data I / O efficiency is effectively improved, enhancing the performance of building large-scale, data-intensive application systems based on microservices.

A system can intercept, by a kernel-space application of a node of nodes, a call from a microservice that is directed to a remote endpoint that is external to a decentralized service mesh architecture, wherein the kernel-space application operates in a kernel space of the node, and wherein the microservice executes in a user space of the node. The system can perform, by the kernel-space application, an authorization check on the call based on the microservice and a virtual address of the remote endpoint identified in the call, to produce an authorization result determine, by the kernel-space application, connectivity information of the remote endpoint based on the virtual address of the remote endpoint identified in the call. The system can relay, by the kernel-space application, network traffic between the microservice and the remote endpoint, based on the authorization result indicating that the call is authorized and using the connectivity information.

The present disclosure provides a serialization and deserialization method of an FPGA-based IPU acceleration service grid. The serialization and deserialization method of the FPGA-based IPU acceleration service grid comprises: determining all message objects of remote procedure calls to be sent and received; allocating a sending descriptor ring and a receiving descriptor ring and configuring a control register through a memory-mapped I / O operation; defining a message format of a remote procedure call through a domain-specific language for serialization and deserialization; when in a transmission mode, creating a sending descriptor and filling it into the sending descriptor ring; the FPGA obtains the sending descriptor from the sending descriptor ring and sends encoded data to a system-on-chip; and the system-on-chip sends the encoded data through a network transmission protocol. The embodiments of the present disclosure solve the problems of performance bottleneck, high delay and high CPU resource occupation in the prior art.