189 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.

The invention discloses an affine formation maneuvering control method for a heterogeneous cluster system and a related device, and relates to the technical field of cluster cooperative control, and the method comprises the steps: obtaining the state information estimation values of all leaders updated by a target follower at the current moment according to the communication relation of the target follower and a distributed observer, according to the state information of the target follower at the current moment and a preset affine formation maneuvering control protocol, control input of the target follower at the current moment is obtained through calculation; according to the state information of the target follower at the current moment, the control input of the target follower at the current moment and a preset dynamic model, position information of the target follower at the next moment is obtained through calculation; and according to the position information of the target follower at the next moment, an affine formation maneuvering control instruction at the current moment is determined, so that the expected formation configuration of the follower can be achieved when the control instruction is executed.

The invention discloses a cluster adaptive path planning method based on geometric PDE and PINN. The method comprises the following steps: firstly, modeling a dynamic model of a fault cluster with time-varying topology; secondly, through Riemannian manifold and gradient evolution based on heat flow, a fault cluster is constructed based on a parabolic PDE system, and a reference trajectory of a cluster system in an obstacle environment is obtained; and learning a steady-state solution of the cluster based on a parabolic PDE system by using a physical information neural network (PINN), and obtaining a fault-tolerant optimal control law of the cluster system based on geometric PDE and PINN under the conditions of time-varying topology, actuator fault and obstacle operation. According to the method, it is ensured that the intelligent agent navigates to the target position from the initial position along the dynamic reference trajectory, meanwhile, endogenous compensation of the fault effect is achieved, and unified solution of fault compensation and motion planning is achieved.

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.

A method includes receiving a dependency chain matrix from each of a plurality of service end points in a multi-cluster system converging the received dependency chain matrices into a converged data matrix, sharing the converged data matrix as a side car module to each of the plurality of service end points, receiving a service request by an external user or service for at least one of the plurality of service end points, validating an authorization for the service request based on the converged data matrix, and generating a label that is appended to the service request and passed downstream to the plurality of service end points.

A multi-node, multi-container cluster system that generates, aggregates, and manages log files from services and components includes a serviceability suite of functions for the system as it performs its tasks of supporting applications for a user. A logging function records events and states of nodes as the applications are run, an audit log processing function processes and compiles the logs for audit purposes, and the relevant log data for is provided in the form of uniform core files. Failure processing functions and debugging tools are provided to help analyze, debug, and correct issues that may be encountered. A support bundle processing function is provided to generate and process the log, debugging, failure, and other information for transfer to vendors or other third parties tasked with supporting the cluster network.

A power distribution network elasticity improvement method based on distributed new energy aggregation relates to the technical field of power systems, and comprises the following steps: S1, establishing a device-cluster-system three-level collaborative architecture, and collecting operation data of distributed energy nodes in real time through digital twin dynamic modeling; s2, dynamically adjusting a virtual power plant aggregation boundary based on a deep reinforcement learning algorithm, and optimizing a resource response strategy; s3, a multi-time-scale optimization framework is adopted, and space-time cooperative elastic enhancement is achieved in the pre-disaster stage, the in-disaster stage and the post-disaster stage; s4, realizing credible evidence storage and compensation liquidation of the resource scheduling instruction through the block chain; aiming at the problems of dynamic response delay and insufficient space-time dimension collaboration in the existing distributed energy aggregation technology, the invention realizes full-chain elastic improvement from minute-level dynamic aggregation to hour-level recovery optimization by constructing a three-level collaboration architecture, and solves the problems of low resource utilization rate and slow after-disaster recovery speed in the traditional method.

The invention discloses a unified representation and dynamic knowledge reasoning method and device for an intelligent cluster system, and the method comprises the steps: determining a decyclization task sub-graph based on a composite task state space model of the intelligent cluster system through the priori information; constructing a real-time data sub-graph by using a real-time data feedback mechanism, and constructing a traceability mode sub-graph based on the decyclization task sub-graph; candidate traceability sub-graphs with the highest matching similarity with the traceability mode sub-graphs are determined from the real-time data sub-graphs to serve as traceability sub-graphs; constructing a reasoning path set based on the decyclization task subgraph, and calculating a probability value of each reasoning path in the reasoning path set by using a path probability model constructed based on a Bayesian conditional probability chain rule; and screening out the reasoning paths of which the probability values are smaller than a preset probability value in the reasoning path set by utilizing a dual path pruning strategy, and adding the reasoning paths containing abnormal nodes in the reasoning path set into an alarm path set so as to provide support for state prediction and risk management of the intelligent cluster system.

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 invention discloses a combined fault monitoring method and device based on a large model, a medium and equipment. The method comprises the following steps: collecting and preprocessing multi-dimensional data; extracting multi-source feature data based on a convolutional neural network and a long-short term memory network; performing redundancy elimination and fusion on the multi-source feature data; and cluster collaborative fault modeling is carried out, and global health assessment is carried out. According to the method, a deep learning large model is established through a convolutional neural network and a long-short term memory network, and effective fault features are automatically extracted from high-dimensional heterogeneous sensor data; through feature fusion of multi-source feature data, dimensionality reduction and fusion are carried out on features extracted from the sensor data, redundant information is removed, and a unified feature vector is generated; through the cluster collaborative fault modeling method, the identification problem of an equipment fault propagation cluster-level modeling path is solved, the globality and comprehensiveness of a fault monitoring system are improved, and fault monitoring of a cluster system is achieved.

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.

Techniques for limiting or reducing storage accessibility “hiccups” in active-active clustered systems that perform large writes. The techniques can include executing, by a surviving, failover, or rebooted node of an active-active clustered system, a specialized recovery protocol that includes treating each large write request from a host computer as a plurality of small write requests while execution of the specialized recovery protocol is in progress, draining all dedicated sub-ubers for a primary and secondary node of the active-active clustered system, and, having completed execution of the specialized recovery protocol, resuming normal treatment of large write requests from the host computer. In this way, the need and complexity of managing large writes and maintaining their corresponding sub-uber information during recovery from a forced reboot, crash, or disaster involving the primary or secondary node can be avoided, and storage accessibility hiccups due to performing the large writes can be limited or reduced.

The invention relates to the technical field of power distribution and power saving of a power system, in particular to an AI cluster system-based CPU (central processing unit) power distribution method for a PC (personal computer), which comprises the following steps of: step 1, collecting long-short period data, step 2, analyzing long-period and short-period habits, step 3, dynamically adjusting habit weights, step 4, detecting and responding to abnormity, and step 5, distributing power to the PC. Continuously optimizing and updating; according to the scheme, long-period and short-period data are comprehensively collected and finely analyzed, so that the accuracy and the dynamic performance of CPU power distribution are realized. Long-period data can capture long-term trends, short-period data can reflect short-term changes, strategies are adjusted in time to adapt to dynamic requirements, time sequence analysis, clustering algorithms and the like are used for deep mining and classification, a simulation environment is constructed to determine an initial scheme, CPU operation and energy utilization efficiency is improved in the mode, different user scene requirements are met, and user experience is improved. And smooth, stable and efficient use experience is provided.

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 domestic VPX server-based cluster system and an application service VIP attachment method, and relates to the field of domestic information systems. The system comprises three computing modules, each computing module adopts a domestic processor, each computing module is provided with a Nomad server and a Consul server, the computing modules are converted into a management node mode, and a one-master two-slave architecture is formed through a master selection mechanism; a Nomad client and a Consul client are respectively operated on each computing module, and the computing modules are converted into computing node modes, so that the computing modules have both management and computing functions. During work, a Nomad cluster is constructed based on a domestic VPX server, mixed arrangement and full-life-cycle management of containerized application services and traditional application services are achieved, resource management and high service availability are supported, and reliable and stable operation of an information system is guaranteed; meanwhile, a VIP function is provided for the application service, and transparent and non-inductive access of an external client or the application service is supported when the application service is migrated.

The invention provides a task scheduling method and device based on Kubernetes, the method is applied to a controller, and the method comprises the following steps: receiving a task submission request; calling a user-defined task segmentation program interface, and splitting the received task into a plurality of subtasks through a task segmentation program; selecting a plurality of target nodes matched with the task from the nodes based on the obtained resource state of each node of the Kubernetes; scheduling the plurality of sub-tasks to the corresponding target nodes, so that each target node processes the received sub-tasks through a task processing program by calling a task processing program interface; and calling a user-defined result merging program interface, obtaining processing results of the subtasks by the target nodes through a result merging program, and merging the processing results to obtain a final processing result. According to the task scheduling method based on Kubernetes, the node resource state can be dynamically sensed, fine-grained segmentation and combination of tasks can be achieved, and the task processing efficiency can be improved.

According to the cluster system simulation solving method and device, the storage medium and the computer equipment provided by the invention, during simulation solving, the cluster system is divided into the multiple independent subsystems according to the topological structure of the cluster system, and the digital model of each subsystem is constructed, so that the model matrix dimension is reduced, and the single-step calculation amount is reduced; and then differential solution is carried out on the digital model of each subsystem to obtain a differential solution result, and then algebraic solution is carried out, so that mutual containment of the two subsystems is avoided, and the speed is increased. Before algebraic solution, dependence parameters of the cluster system are determined according to the controlled relationship among the subsystems, and whether the cluster system has an algebraic ring is determined and judged based on the dependence parameters; if yes, carrying out iterative solution on each digital model by adopting a Newton-Raphson iterative algorithm to avoid error accumulation caused by closed-loop dependence; and if not, sequentially solving the algebraic equations in the digital models by adopting a display function, and improving the solving efficiency to a millisecond level.

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 invention discloses an electronic device, a computing cluster, a heat dissipation management method and a storage medium, and relates to the technical field of heat dissipation management.The electronic device comprises a computing node and a switching node, and the computing node is connected with the switching node. The computing node comprises at least one first device with a computing attribute and / or at least one second device with a transmission attribute, the first device executes a computing task in artificial intelligence training, and the second device executes a transmission task in artificial intelligence training; the switching node comprises a control module, and under the condition that the heat dissipation speed of each device is determined according to the calculated heat of the first device and / or the transmission heat of the second device, the corresponding heat dissipation device is controlled through the control module to dissipate heat of each device, so that each device is located in the corresponding target temperature interval. Therefore, the problem that the system stability is poor due to the fact that an existing thermal management method of a cluster system is poor in sensing capacity and cannot dynamically regulate and control equipment resources is solved.