124 results about "Cluster systems" patented technology

A multi-node, multi-container cluster system that generates, aggregates, and manages log files from services and components to be used for audit logs and to debug and perform other serviceability tasks provided by a vendor of the cluster system. Logs are collected from all components of the system and aggregated into a consistent format for user analysis and debugging. Embodiments provide a comprehensive way to parse and index vast numbers of log files that can then be packaged and displayed to a user in a way that facilitates analysis and debugging and / or efficient input to appropriate debugging programs.

Providing issue resolution in a cluster system by monitoring system operation to detect occurrence of a system error, and automatically generating, upon detection of the error condition, a core file for a user node. The core file captures a current memory state of a respective node, where the current memory state comprises system statistics, system information, and logs. An intelligent core debugger extracts information from a core file to generate a core file report that is sent to a vendor for a quick determination of whether sufficient information is in the report to allow the vendor to recommend a fix, or whether further information from is required, including the core file itself, if necessary. This prevents the need to send an entire core file to a vendor in every instance of a system fault.

The invention discloses a system quick recovery method and system based on system mirror image management, and the method comprises the steps: initializing a boot partition of a functional blade in a heterogeneous cluster system and a boot flag bit of the boot partition, the boot flag bit being used for BIOS reading to select a corresponding boot partition when the functional blade is started; the method comprises the following steps: triggering a job operating system or a boot operating system of a target function blade to restart through a soft shutdown interface, and modifying a start boot flag bit in a BMC (Baseboard Management Controller) of the target function blade, so that the target function blade is sequentially restarted and switched into the boot operating system from the job operating system; and executing system quick recovery or backup for the target system partition under the boot operating system, and then restarting and switching to run under the specified job operating system so as to complete system quick recovery or backup. According to the method, the characteristics of the heterogeneous cluster system are combined, and automatic and flexible system mirror image fast rollback recovery is achieved when the heterogeneous cluster system has a serious fault.

The embodiment of the invention provides a cluster collaborative navigation method, a control system and a storage medium. The method comprises but is not limited to the technical field of navigation. The method comprises the following steps: in an upper layer module of a gene regulation and control network, determining a form boundary curve according to first position information of first execution equipment, second position information of second execution equipment and third position information of an obstacle; in a lower layer module of the gene regulation and control network, determining a tangential propulsion speed component and an offset correction speed component according to the form boundary curve and the first position information; and in a lower layer module of the gene regulation and control network, according to a visual adjacent distance regulation speed component, a tangential propulsion speed component and an offset correction speed component of the visual projection field, determining a target linear speed and an angle parameter. According to the embodiment of the invention, collaborative navigation and dynamic form maintenance of the cluster system can be realized.

The invention relates to the technical field of distributed synthetic aperture radar (SAR) imaging detection and cluster guidance, in particular to a mobile platform bistatic SAR moving target imaging and positioning parameter estimation method and system, through the method provided by the invention, distance migration correction of a moving target is realized by adopting distance frequency inversion transformation, and the positioning accuracy of the moving target is improved. According to the method, an accelerated multi-component QFM signal parameter estimation method is optimized by adopting a genetic algorithm in azimuth, and finally, state parameter estimation on a maneuvering target with acceleration is realized by adopting a combined aperture method based on each order of estimated Doppler parameters, so that the condition of failure in the prior art is avoided. According to the method, imaging and positioning of the maneuvering target by the maneuvering platform bistatic SAR are achieved, the application range of the current bistatic SAR is expanded, and the method mainly relates to point multiplication and fast Fourier transform (FFT) and is high in operation efficiency, so that the method is suitable for the scene that terminal guidance application is conducted on the moving target by a cluster system.

The invention discloses a computing node upgrading method and device, a storage medium and electronic equipment, and relates to the technical field of computers.The computing node upgrading method includes the steps that when a computing cluster receives an upgrading request, virtual machines serve as target virtual machines, computing nodes where the target virtual machines are located serve as source computing nodes, and the source computing nodes serve as source computing nodes; according to the operation information of the target virtual machine and the architecture type of the source computing node, screening out the target computing node meeting the operation condition from the computing cluster; then, according to the migration sequence indicated by the priority of the virtual machines of the computing cluster, the virtual machines on all the source computing nodes are migrated to the corresponding target computing nodes, and the computing nodes with all the migrated virtual machines are upgraded, so that the problems that the adaptability of virtual machine migration decisions is insufficient, and the efficiency is high are solved. The technical problem that the service operation stability is low in the upgrading process of the computing cluster system is caused is solved, and the technical effects of improving the adaptability of the virtual machine migration decision and further improving the service operation stability in the upgrading process of the computing cluster system are achieved.

The application is suitable for the technical field of data storage, and provides a data disaster recovery method and device, comprising the following steps: determining a fault type in a distributed cluster system and a range of data to be recovered; acquiring state parameters of multiple normal nodes in the distributed cluster system, and service sensitivity features and data block access features of the data to be recovered; performing data preprocessing operations; respectively adopting a service sensitivity evaluation model and a node state evaluation model to determine a data recovery priority list and a node priority list; determining a fault processing strategy according to the data recovery priority list and the node priority list; and executing the fault processing strategy. Through the above method, the real-time evaluation algorithm of the heterogeneous big data node is increased on the basis of dynamically updating the data recovery priority based on the service sensitivity evaluation model, and compared with the traditional technology, the fault perception of the business system can be reduced while the data disaster recovery efficiency is improved.

The embodiment of the invention relates to a control method and device for a multi-cluster system, computer equipment and a storage medium, and the method comprises the steps: obtaining the resource state information of each cluster from a target database when a task is received; determining a first cluster from the plurality of clusters according to the resource state information of the plurality of clusters, so as to allocate the task to the first cluster, and to allocate the task to the first cluster; when it is detected that the first cluster breaks down, resource state information of each cluster is obtained again, and a second cluster is determined from the multiple clusters except the first cluster according to the resource state information obtained again; and distributing the tasks which are not executed completely in the first cluster to the second cluster for continuous execution. Therefore, cluster resources can be reasonably allocated when the multi-cluster system processes tasks, so that system-level fault tolerance is realized and service continuity is guaranteed through multi-cluster redundancy and automatic failover when a single cluster fails.

The invention discloses a micro-grid cluster dimension reduction method and system based on scene self-adaption and topology maintenance. Aiming at different types of energy, constructing a steady-state characteristic index system and decoupling the steady-state characteristic index system into a plurality of groups of characteristics; the method comprises the following steps: clustering system time series data, constructing an operation state manifold diffusion matrix, clustering by using a fuzzy clustering method based on diffusion distance, and outputting an optimal scene division result; constructing a Wasserstein distance matrix by using the steady-state characteristic index of each type of energy, defining a scene objective function and solving an optimal index weight; and calculating a scene membership degree and a fusion index vector weight according to the real-time operation state, weighting the steady-state characteristic index system, and outputting a weighted characteristic matrix. And performing multi-scale topology analysis on the weighted feature matrix, constructing a topology stability constraint, and aggregating the power grid equipment by adopting a hierarchical clustering method based on the topology stability constraint. According to the technical scheme, the internal evolution rule of the operation state can be accurately captured, so that the aggregation result better meets the actual operation requirement.

The invention discloses a multi-target optimization tracking control method for a heterogeneous unmanned cluster system based on MADDPG, and the method comprises the following steps: building a dynamic model of the heterogeneous unmanned cluster system, and carrying out the isomorphism of the heterogeneous unmanned cluster system through speed estimation; establishing a communication topology model of the heterogeneous unmanned cluster system, designing an adjacent matrix weight, and quantifying the information interaction strength between the heterogeneous unmanned cluster systems; describing a plurality of optimization objectives of the heterogeneous unmanned cluster system and executor saturation constraint conditions of the optimization objectives; modeling a multi-target optimization tracking control problem of the heterogeneous unmanned cluster system into a reinforcement learning problem, and designing MADDPG-based tracking control algorithm elements; and a learnable weight parameter is established, a target weight coefficient is updated online by using gradient back propagation, and dynamic optimization of multi-target weight is realized. Through an adaptive target weight adjustment mechanism and a saturation constraint and heterogeneous unified modeling framework, the tracking control problem under the influence of multi-target conflict, actuator saturation constraint and heterogeneous characteristics is solved, and the cooperative tracking control effect and robustness of a heterogeneous unmanned cluster system in a dynamic environment are improved.

The invention provides a server cluster load balancing state tracking analysis method, and relates to the technical field of servers, and the method comprises the steps: collecting a load migration triggering moment, the number of control messages generated by single migration, and the number of active connections to be migrated of each server from each server in a cluster, meanwhile, the bandwidth bearing upper limit of a network control channel is obtained, migration triggering events are arranged according to the time sequence, and a list of the number of triggered migration servers is obtained; the migration scheduling strategy of each server in the load balancing system is updated by adopting the target migration coordination delay interval, a migration rhythm coordination confirmation signal is obtained by monitoring the network congestion change, and whether the cluster load balancing state is recovered to a safe range is verified by combining a high-risk time period list, so that the stability and efficiency of load migration are improved, and the load balancing efficiency is improved. And a reliable flow coordination guarantee is provided for a large-scale cluster system.

The invention relates to a mimicry computing cluster system environment deployment tool and method, and relates to the field of advanced computing, and the tool comprises a mimicry computing node management interface; the mirror image packaging component is used for providing a system environment installation mirror image packaging function for the mimicry computing node; the mimicry computing node deployment service component set is used for providing universal basic services for the deployment process; the service arrangement component provides a self-defined system environment deployment process and an installation configuration scheme, and performs error detection on the deployment configuration scheme generated by arrangement; the mimicry computing resource sensing registration component is used for performing information acquisition and sensing on each computing node in the mimicry computing cluster; and the mimicry computing node initialization component is responsible for executing a deployment management command. According to the method, the requirements of mimicry computing cluster system environment large-scale deployment, heterogeneous resource adaptation, multi-field application deployment and efficient operation and maintenance can be met.

The invention discloses a self-adaptive event triggering cooperative tracking control method for an unmanned cluster system under resource limitation. The method comprises the following steps: establishing a dynamic model of a leader and a follower for a heterogeneous unmanned cluster system; constructing a distributed observer for the follower; determining a state measurement error and an internal dynamic variable of the follower, determining a dynamic event triggering condition of the follower, and constructing a self-adaptive event triggering controller of the follower; when the state measurement error meets or does not meet the dynamic event triggering condition, the adaptive event triggering controller adopts different means to obtain the system observation state of the neighbor node and updates the adaptive event triggering controller; and the follower realizes cooperative tracking control on the leader based on a system observation state. According to the method, the communication burden is effectively reduced, and an efficient and reliable solution is provided for cooperative control of the heterogeneous unmanned cluster system in a complex environment.

This application provides a multi-cluster system, an autoscaling method therefor, a computing device cluster, a medium, and a computer program product. The multi-cluster system includes: a plurality of clusters in which an application is deployed, where a plurality of instances of the application are distributed in the plurality of clusters, each cluster includes a native scaling module, and the native scaling module is configured to increase or decrease a quantity of instances in the cluster to implement single-cluster instance autoscaling; and a management apparatus, configured to manage the plurality of clusters. The management apparatus includes: a status detection module, configured to detect statuses of the plurality of clusters; a scaling configuration management module, configured to manage native scaling configurations of the plurality of clusters; and a scaling execution module, configured to: when the status detection module detects that single-cluster instance autoscaling cannot be implemented, perform cross-cluster instance autoscaling. According to this application, single-cluster autoscaling and cross-cluster autoscaling can be implemented, migration costs can be reduced, large-scale reconstruction of an existing system can be avoided, and compatibility can be implemented.

A method and system for AI-based collaborative energy-saving valve cluster control, belonging to the interdisciplinary field of industrial process control and artificial intelligence, comprises the following steps: First, modeling and defining the optimization problem of the valve cluster system; second, constructing an intelligent optimization model; third, training the intelligent optimization model based on federated learning; and fourth, online collaborative control and continuous learning. This is achieved through an energy-saving valve cluster control system, including a valve cluster system modeling module, an intelligent optimization model construction module, an intelligent optimization model federated learning training module, and a collaborative control and continuous learning module. This invention enables direct and dynamic mapping between the valve cluster system state and the optimal valve opening vector, dynamically and selectively integrating key local information of valves, edges, and nodes to achieve precise and adaptive collaboration; significantly reducing the total energy consumption of the system and achieving optimal global energy efficiency and dynamic continuous optimization of the valve cluster system.

The application discloses a kind of heterogeneous unmanned cluster system security immune control methods under distributed denial of service attack.The application uses event triggering mechanism to avoid continuous information interaction between followers,achieves discrete communication,and effectively excludes Zeno phenomenon,gets rid of the limitation of limited communication bandwidth in actual situation;In addition,getting rid of the design requirement of distributed observer in traditional method,avoiding the extra information transmission caused by observer state variable;And based on the design of controller with matching idea to get rid of the prior information constraint of system model,can realize state tracking under the condition that system model is uncertain and different communication links are attacked independently;At the same time,communication channel equivalent decay rate is introduced,under the condition that each communication link is attacked independently,decay condition is analyzed,system model uncertainty and network attack diversity in actual situation are effectively coped with.

Systems and methods described herein relate to the assignment of user devices to workload clusters. Resource utilization on a plurality of user devices is monitored. A device agent on each user device may be used to monitor the resource utilization. A workload execution request identifies resource requirements of a cluster workload. The workload execution request is assigned to a cluster based on the resource requirements of the cluster workload and the resource utilization on the plurality of user devices. The cluster comprises the plurality of user devices. The workload execution request is caused to be executed on the cluster. Each user device executes user workloads and a respective portion of the cluster workload.

This application discloses a flow control method and related equipment. The method proposed in this application can acquire flow pressure data of at least one functional module in a cluster system; based on the flow pressure data of each functional module, overload detection is performed on the flow of each functional module; when overload is detected in at least one functional module, a target functional module for flow control is identified; based on the flow control reference information and flow control history information of the target functional module, a target flow control policy corresponding to the target functional module is determined; based on the target flow control policy, a target generation rate for the access permission credential information of the target functional module is determined; based on the target generation rate, flow control is performed on the target functional module, thereby improving the reliability of flow control and achieving flexible and precise flow control.

The invention provides a cluster system comprising a computing unit and a switching unit and a task processing method and device, and relates to the technical field of computers. The cluster system comprises N computing platforms, and each computing platform comprises a switching unit, a computing unit, a power supply unit, a monitoring management unit and a controller. The computing unit comprises at least one of a PC, a CPU, a GPU and a neural network computing module; the switching unit comprises a first type switching module which is used for connecting a hardware or software interface of a network, is responsible for sending, receiving and processing data, and comprises but is not limited to a gigabit Ethernet interface; the second type of switching module provides support for high-speed serial data transmission, adopts high-speed data interfaces, and comprises but is not limited to a Serdes interface and an LVDS (Low Voltage Differential Signaling) interface; the third type of switching module is a high-bandwidth and low-delay data interface which comprises but is not limited to an MCIO interface and a PCIe interface; and the fourth type of switching module is a high-bandwidth and high-density data transmission interface which comprises but is not limited to a QSFP interface and an OSFP interface.

The present application relates to a kind of multi autonomous guide vehicle (AGV) cluster security path tracking control method of resisting false data injection attack, belong to control engineering technical field.For the problem that system stability and safety are threatened by false data injection attack in unreliable network, the present application introduces dynamic event triggering mechanism, only updates control input when necessary, reduces communication burden and energy consumption;Design adaptive state estimator, recover normal system signal from tampered sensor and actuator signal;Adaptive attack compensation mechanism is built, to inhibit the negative influence of attack on system performance.The present application fully considers the application requirement of multi-AGV cluster system in unreliable network environment, by introducing dynamic event triggered control, designing adaptive state estimator and building adaptive attack compensation mechanism, false data injection attack can be effectively resisted, and strong guarantee is provided for multi-AGV cluster system security path tracking control.

Embodiments of the present disclosure provide a graphics processing unit (GPU) cluster system and a server. The system comprises: a computing node and a switching node; a first board where the computing node is located and a second board where the switching node is located are orthogonally arranged in a cabinet, and each GPU of the computing node is interconnected with each switching chip of the switching node respectively by means of an orthogonal OD connector.

A processing unit of a first computing node is configured to send a first request to a second computing node, and a detection unit of the first computing node is configured to, when the first request times out, send a first message to the processing unit of the first computing node. The first message includes one or more of long-latency timeout information and blocked path information. When a response time of the second computing node to the first request is greater than a first threshold, the first request times out. The first threshold is determined based on a plurality of response times, and the plurality of response times are respectively response times of the second computing node to a plurality of requests that have been sent by the first computing node.

The invention discloses a data processing method and device, equipment and a storage medium, and belongs to the technical field of computers. The method specifically comprises the following steps: acquiring a hardware resource use condition and a draft historical acceptance rate; wherein the hardware resource use condition is determined on the basis of the operation condition of the GPU cluster system executing reasoning of the large language model, and the draft historical acceptance rate is determined on the basis of the reasoning result of the large language model; obtaining a speculation parameter adjustment strategy of a speculation decoding algorithm by utilizing a reasoning acceleration strategy prediction model based on the hardware resource use condition and the draft historical acceptance rate; and based on the speculation parameter adjustment strategy, performing optimization processing on reasoning of the large language model.

The embodiment of the invention discloses an AI full stack performance tuning tool for top-down analysis. A front end, a middle end and a rear end are separated; the front end is used for visually displaying summarized information needing to be concerned on each level of the AI full stack and specific execution information of different granularities; the middle end is used for connecting the front end and the rear end and providing a plurality of configurable visual components; and the rear end is used for receiving the data files in various data formats, processing the data files and storing the processed data files in a plurality of mutually decoupled databases for the middle end to query and call after configuration. The method has the advantages that the working efficiency of developers of all levels can be improved; a developer can be assisted to realize more accurate and deep performance optimization; the personalized configuration requirements of different levels of development on the front-end interface can be met; the large model deployment and use cost can be reduced, and the utilization rate of the computing power cluster system and the computing power chip is improved.

The application relates to the technical field of communication, in particular to a high-availability MCS cluster system supporting an SECS protocol, which comprises an ORACLE database cluster, an MQ cluster, a plurality of MCS servers and a plurality of MCS drivers, the MCS servers and the MCS drivers communicate through the MQ cluster, the ORACLE database cluster realizes data sharing of each application node, and a database service layer is built by adopting an ORACLE RAC mode; the application has clear architecture and strong adaptability, can flexibly assemble the MCS servers and the MCS drivers to adapt to other external control systems or device systems, and can be multi-dimensionally expanded horizontally according to the busy degree of FAB logistics conveying, is easy to construct and convenient to manage; the MCS message processing application and the device communication protocol are fully decoupled, the stable production of the FAB environment can be guaranteed to the maximum extent, the system development platform is above.NET5, is cross-platform compatible, supports the deployment of LINUX and WINDOW platforms, and the node instances required to be deployed are consistent under various scenes and platforms.

The application relates to the technical field of high-performance computing, and particularly discloses a method for predicting the performance of an application program and a computing device. The method comprises the following steps: constructing a performance characteristic data set based on the running parameters of an application program in each job of a cluster system; determining similar characteristic data from the performance characteristic data set based on the running parameters of the application program on a single computing node of the cluster system; classifying the similar characteristic data according to the number of computing nodes, generating simulation characteristic data by using an application performance prediction model based on the number of similar characteristic data in each classification; generating an application data set by using the similar characteristic data and the simulation characteristic data; fitting the relationship between the number of computing nodes and the running time by using a polynomial regression algorithm based on the application data set; and predicting the running time of the application program on each computing node of the cluster system by using the fitted relationship. According to the application, accurate computing power use selection suggestions can be provided for the application program.

The invention belongs to the field of micro-grid cluster system coordination control, and particularly relates to a wind and light storage multi-source micro-grid cluster coordination control method based on a sparrow search algorithm. Aiming at the defect that the convergence speed of the existing sparrow search algorithm is difficult to meet the real-time requirement of micro-grid coordination control, the invention adopts the following technical scheme: the wind-light storage multi-source micro-grid cluster coordination control method based on the sparrow search algorithm comprises the following steps of: formulating respective control models according to different controllers in a wind-light storage multi-source micro-grid; setting PI parameters of each controller according to the control model and the system response; an improved sparrow search algorithm is adopted to update PI parameters of each controller of the micro-grid, including mapping a vector to a symmetric positive definite manifold through a function, and calculating an optimized update step length through Riemannian metric; updating the position according to the optimized updating step length; and judging whether a convergence condition is met or the maximum number of iterations is obtained. The method has the beneficial effects that the convergence speed and precision are improved.

The invention provides a global optimization design method of a cluster system under a network attack, which comprises the following steps: for adjustable parameters in a controller, defining a system overall cost function which comprehensively considers transient performance, steady-state performance and control cost of the system, and converting an optimal design problem into a multivariable optimization problem; by solving a multivariate optimization problem, a group of optimal solutions enabling the overall cost function of the system to be minimum are sought; the uniqueness of the optimal parameter is proved; and substituting the optimal value of the sum into control to obtain an optimal design. On the basis of keeping the steady-state performance of the cluster system, the control parameters of the system are globally optimized, the control parameters of the whole system can be coordinated, and the performance of the system is further improved.

The invention relates to the technical field of operation and maintenance management in a distributed computing environment, and discloses a federated multi-cluster unified log and monitoring method and system, and the method comprises a log data collection module, a feature analysis module, a monitoring strategy generation module and a monitoring execution module. The log data acquisition module acquires historical log records, performance indexes and abnormal event data through a cross-cluster interface; the feature analysis module performs cleaning and feature extraction on the data by adopting a time window method and quantile calculation to generate log, performance and abnormal feature parameters; the monitoring strategy generation module performs strategy retrieval based on the knowledge graph space, and dynamically matches an optimal monitoring strategy; and the monitoring execution module carries out monitoring according to the strategy and analyzes the result. Uniform collection, intelligent feature analysis and self-adaptive monitoring of log data in a multi-cluster environment are achieved, the operation and maintenance efficiency and monitoring precision of a cross-cluster system are improved, and the method is suitable for distributed scenes such as cloud computing and big data.

Systems and methods are provided for simplifying the generation / application of machine learning models in a network or other deployment of elements or objects of interest. Data (which can be multi-variate, high dimensional, time-series) regarding or associated with such objects may be represented as random matrices, which can then be transformed diagonal variance matrices. Upper and lower confidence bounds can be determined with which to test similarity between the now, diagonal matrices. Based on the determined similarity or dissimilarity, one or more clusters of matrices, representative of the objects of interest, can be determined. In this way, machine learning models can be trained and developed to be operationalized for the clustered matrices (objects) rather than individual matrices (objects).