1105 results about "High availability" patented technology

The invention relates to the technical field of cloud computing and artificial intelligence, in particular to a disaster recovery drill and performance evaluation system and method for multi-cluster distributed training. In order to solve the problem of disaster recovery and performance evaluation in multi-cluster distributed training, the method comprises the steps of environment analysis and modeling, fault injection and drill control, cross-cluster scheduling and resource monitoring, performance data acquisition and index aggregation, training result evaluation and tuning closed loop and the like. Multi-cluster environment elements are identified, a fault and index mapping relation is constructed, faults are injected by using a fault injection tool, and task scheduling, resource monitoring and performance data acquisition and analysis are realized by means of a multi-cluster management and monitoring tool, so that an adjusting and optimizing strategy is formed, and closed-loop management is realized. According to the method, faults can be dynamically simulated in a multi-cluster environment, the training performance is monitored, the disaster recovery and recovery efficiency is evaluated, the training efficiency and robustness of the system are improved, and the requirements for high availability and high performance in the fields of finance, automatic driving and the like are met.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a processing environment switching and recovery method, device, equipment and medium. The method comprises the steps that multi-source heterogeneous data in a main processing environment and a standby processing environment are acquired, and the system fault probability is obtained through multi-model collaborative prediction; a dynamic threshold value is generated in combination with a historical service period mode and a real-time service load, when the fault probability exceeds the threshold value, a switching strategy is generated based on the fault scene knowledge base and the service priority, and flow scheduling between the main processing environment and the standby processing environment is executed; and monitoring the business index of the standby processing environment during the scheduling period, and triggering the fusing rollback when the business index is lower than the health standard. According to the method, the fault identification precision is improved through multi-source data fusion and multi-model prediction, adaptive scheduling is realized in combination with a dynamic threshold and a switching strategy, and fusing rollback is triggered to guarantee high availability and data consistency, so that the continuity and stability of key services are enhanced.

The invention relates to a server cluster monitoring system based on multi-node collaboration and an implementation method thereof, a dynamic topology network module is configured to reconstruct a connection topology among monitoring nodes in real time according to node performance and link quality, support mixed configuration of a star type, a ring type and a net structure, and realize multi-node collaboration. Multi-dimensional data capture from a physical layer to an application layer is realized through a cross-level index acquisition module based on an integrated hardware sensor interface and a virtualization layer probe, and each node is enabled to perform collaborative reasoning through parameter encryption sharing through a decision model based on federated learning. A monitoring task fragmentation strategy is dynamically adjusted through an adaptive elastic fragmentation unit according to network delay and load fluctuation, and an abnormal event association rule base is updated in real time through an incremental knowledge graph construction unit. High availability and elastic expansion are realized through a multi-node collaborative architecture, the monitoring efficiency is improved in combination with dynamic load balancing and hybrid detection, and an intelligent multi-level response mechanism is constructed to guarantee the service continuity.

The invention discloses a memory enhanced script generation agent system based on RAG, and belongs to the technical field of artificial intelligence. The pre-learning and intensive training module takes a terminology library, an ambiguity explanation library and a script sample knowledge library as pre-learning blocks, adopts an intensive learning algorithm to train the generation module, and meanwhile, quantifies specific indexes to form a script scoring rule; the self-learning script generation module is used for realizing script generation; the long-term memory and knowledge tree management module is used for extracting, coding and storing key information layer by layer; the script evaluation and feedback closed-loop mechanism is used for grading and scoring the generated script through a preset quantitative evaluation index and feeding back the score to the self-learning script generation module; according to the RAG-based memory enhanced script generation agent system provided by the invention, flexible generation of a final high-quality script can be realized through cooperation of multiple agents, the sense of makeup is reduced, and the usability and usability are improved.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention discloses a high-availability testing method and system for task scheduling of a computing power host management platform, belongs to the technical field of high-availability testing of computers, and aims to solve the technical problems that in the prior art, high-concurrency scene testing coverage rate is insufficient, cross-domain scheduling, failover and self-healing scene verification are imperfect, and testing efficiency is high. According to the technical scheme, the method structurally comprises the steps of hybrid load testing, wherein the elastic capacity expansion and contraction capacity and the performance stability of a system are verified by simulating a long-time stable load and burst flow scene; cross-regional intelligent scheduling: tasks are dynamically scheduled based on cost, time delay and regional strategies, and optimal resource allocation and service continuity are ensured; resource fault migration recovery: simulating node-level or region-level faults, and verifying the automatic detection, migration and recovery capabilities of the system; monitoring and data collection: collecting affairs, resources and stability indexes in real time, and providing multi-dimensional data support for verification; and analysis and optimization: identifying a bottleneck based on test data to generate optimization suggestions, and continuously improving the high availability of the system.

The invention discloses a reliable communication system based on a publishing and subscribing mode in a satellite-ground weak network environment, and aims to solve the problems of high round-trip delay, limited link bandwidth, high instantaneous packet loss rate, easiness in blocking and switching influence of links and the like in a data transmission process between a low-orbit satellite and a ground station. A publishing and subscribing model is introduced into an application layer to realize service logic decoupling, deep fusion with an underlying enhanced QUIC transmission protocol is realized, and mechanisms such as message persistence, breakpoint resume, forward error correction and connection migration are combined, so that stable, orderly, complete and safe transmission of service data can still be ensured under the conditions of link instability, quality fluctuation and communication interruption, and the service data transmission efficiency is improved. And high availability and continuity of a satellite-ground communication task under a weak network condition are realized.

A cloud-based architecture includes a cloud-based network management system (NMS) that provides the management plane and one or more cloud-based NAC systems that provide NAC services, including end-point device authentication. The cloud-based NAC system may have multiple groups of authentication server instances. Each group may have multiple authentication server instances to allow the authentication service provided by the group to serve more client devices than a single authentication server instance alone. Different Groups can be configured differently to serve different tenant sets.

The invention relates to a cloud computing extension cluster high availability method based on a dynamic fault domain and intelligent scheduling, and relates to the field of cloud computing extension cluster high availability. According to the method, hardware health data and network performance indexes of physical nodes are collected in real time, and the fault correlation degree between the nodes is calculated based on the hardware health similarity and a network topology attenuation factor; dynamically generating and updating a logic fault domain topological structure according to the fault correlation degree and a preset threshold value; based on the logic fault domain topological structure and a preset SLA strategy library, executing a virtual machine scheduling decision of multi-objective optimization so as to balance business service quality, fault domain risk and resource cost; and in response to the detected fault event, triggering a corresponding hierarchical migration process according to the service priority and the fault level. According to the method, closed-loop linkage of real-time reconstruction and intelligent scheduling of the dynamic fault domain in the cross-region cloud computing extension cluster can be realized, and the disaster tolerance capability and the resource utilization rate of the system are remarkably improved.

The invention discloses a storage cluster, a system, a data processing method, equipment, a medium and a product, and relates to the technical field of data storage. A plurality of devices are arranged in a controller unit, a switch unit and an extended memory unit; a plurality of switches of the switch unit are correspondingly connected with a plurality of extended memories of the extended memory unit, and copy data of a plurality of controllers are respectively stored in the extended memories. The technical problem that the high availability of a storage system is poor due to the fact that the expansibility of the number of controllers and the number of data copies is poor is solved, and the purposes that memory cache data of the controllers are decoupled from DRAM memories in the controllers to external extended memories, deployment operation is simplified, N-1 controllers are allowed to fail at the same time, and the reliability of the controllers is improved are achieved. The data access processing can be completed under the condition that the M-1 switches simultaneously fail and / or the extended memories corresponding to the P-1 data copies fail, so that the technical effect of high availability of the storage system is improved.

The invention discloses a data asset secure storage method based on a block chain technology, and the method comprises the following steps: data preprocessing and fragmentation storage: carrying out the preprocessing of to-be-stored original data assets, extracting metadata information, and generating a unique hash identifier; through multi-chain collaborative storage, dynamic hierarchical encryption and cross-chain redundant backup, the security of data assets is remarkably improved, fragment storage is combined with a heterogeneous block chain network, the data storage efficiency is improved, and single-point failure and centralized attack risks are avoided; the anti-cracking capability is enhanced through dynamic encryption and distributed key management; a cross-chain synchronization mechanism ensures high availability of data, so that the data can be efficiently circulated in different block chains; the operation and maintenance cost is reduced through automatic verification and recovery driven by the intelligent contract, the auditing tracking and life cycle management functions meet the compliance requirement, and the method is suitable for data storage scenes in the high-sensitivity fields of finance, medical treatment and the like.

The invention relates to the technical field of distributed data storage, and discloses a data index construction method for distributed data storage, comprising the following steps: constructing a hypergraph model of a distributed storage system through a high-order graph theory, the hypergraph model comprising a plurality of storage nodes, a plurality of data blocks and a plurality of query requests, the method comprises the following steps: performing load balancing optimization on storage nodes and a query request on the basis of a minimum cut theorem in a high-order graph model, mapping data blocks and the storage nodes to a consistent hash ring according to a consistent hash algorithm, reducing data migration through a virtual node mechanism, storing the data blocks on a plurality of storage nodes in a redundant manner by adopting a redundant coding method, and storing the data blocks on the storage nodes in a redundant manner. And a load distribution strategy of the storage nodes is adjusted through a game theory model, and load adjustment is optimized by adopting an optimal control method. The method can ensure high availability, expansibility and reliability of the system while improving the efficiency of the data storage system, and is suitable for a large-scale distributed data storage system.

In accordance with example implementations, a process includes associating a secondary computer system with a primary computer system. The secondary computer system includes a first virtual machine, which is hosted by a secondary set of physical computer nodes. The primary computer system has a primary role, and the primary role corresponds to a minimum physical resource allocation. The secondary computer system is associated with a secondary role, and the second computer system is provisioned with a second physical resource allocation, which is less than the minimum physical resource allocation. The process includes, responsive to the primary computer system failing and without rebooting the secondary computer system, preparing the secondary computer system to take over the primary role. The preparation includes adding at least one physical computer node to the secondary set of physical computer nodes to cause the second physical resource allocation to meet or exceed the minimum physical resource allocation. The process includes, responsive to the primary computer system failing and without rebooting the secondary computer system, causing the secondary computer system to take over the primary role.

The invention relates to a computing power resource dynamic scheduling and monitoring method based on a cloud platform. The method is suitable for an intelligent scheduling scene of a high-performance GPU cluster. The method comprises seven steps of task portrait modeling, GPU node state acquisition, resource trend prediction, SLA tracking, scheduling scoring and deployment, operation monitoring and task migration, and SLA feedback optimization. According to the system, task semantics are represented by constructing task vectors, node health states, topology affinity, SLA historical performance conditions and resource prediction risks are fused, a multi-factor adjustable scheduling scoring mechanism is constructed, and second-level perception and task thermal migration of high-temperature nodes are achieved. Compared with a traditional Kubernetes static scheduling scheme, the method has the advantages that the GPU utilization rate, the task SLA achievement rate and the system stability are remarkably improved, the learning ability, the self-adaptive ability and the high availability are achieved, and the method is an intelligent scheduling closed-loop system oriented to AI reasoning and training scenes.

A technique monitors input / output (I / O) and storage ownership takeover activity patterns to detect degraded performance of a highly available and fault tolerant application executing in a multi-site environment. Multiple instances of the application execute in different containers or pods running on virtual machines (VMs) of a compute layer within a containerized (e.g., Kubernetes) clustering configuration that extends across clusters of the sites. A storage layer of the clusters provides shared storage to the pods running on the VMs across the multiple sites. One of the sites is configured as an active storage site configured to receive and service I / O requests from the compute layer. A single instance of the application is active at a time and configured as a “compute owner” of the shared storage to issue the I / O requests to the shared storage. The compute owner and active storage site may not be co-located on the same site, leading to excessive I / O and storage ownership takeover activity patterns indicative of degraded performance. Upon detecting such patterns, the technique automatically triggers a storage failover to ensure that compute owner and active storage site are co-located at the same site.

The invention relates to the technical field of data center high availability, in particular to a cross-data center fault isolation and switching method and system in a multi-tenant environment. The resource mapping table is constructed by taking the tenants as the minimum control units, the affected tenants are accurately identified, logic isolation is executed, the problem of waste of full-tenant service migration resources caused by node-level or cluster-level switching in the prior art is avoided, and the fault influence range is minimized. The optimal data center is dynamically selected by combining the multi-factor objective function with the tenant SLA level, the defects that an existing scheduling strategy is opaque and tenant priorities cannot be distinguished can be overcome, and it is ensured that key tenant services are preferentially recovered. And finally, through combination of a gray takeover mechanism and health feedback confirmation and multi-dimensional recovery verification after migration, the condition that an existing recovery mechanism is extensive can be changed, the fault processing precision and the resource scheduling efficiency of the data center in a multi-tenant environment are remarkably enhanced, and the service continuity is guaranteed.

Current OT security solutions mostly provide defensive security involving monitoring and network segmentation. However, offensive security, proven effective in IT systems, is not easily applicable to OT systems due to its sensitive nature and high availability requirements. To conduct offensive security measures in OT networks, the concept of Digital Twins emerges as a viable solution but with limited progress in developing virtual replicas that accurately simulate real environments. Further, the process of creating such replicas is inherently complex and time-consuming, often involving extensive manual work. The present invention discloses an environment named as “Battle Twin,” a specialized playground designed for breach and attack simulations specifically designed for OT networks. Battle Twin focuses on mimicking the security posture of the underlying network rather than replicating its production or behavioral aspects. By leveraging Battle Twin, security experts may effectively test and evaluate offensive security strategies without even touching real OT environments.

The invention belongs to the field of query optimization, and particularly relates to a fragmentation storage and query optimization method and system for a high-concurrency database, and the method comprises the steps: analyzing business features through a preset decision tree model, and selecting an optimal fragmentation key, constructing a fragmentation rule engine and initializing a database in combination with hash, range and list fragmentation rules and a fragmentation splitting-merging strategy; generating a query abstract syntax tree by using an ANTLR4 analyzer, and detecting whether a preset Cube is hit or not to directly return a result; if not, dynamically routing to a target fragment list based on a fragment key field, load balancing and a failover strategy, accelerating data retrieval in combination with a high-frequency index, and combining fragment-level results through a T-TopK algorithm; according to the method, the self-adaptive matching of the fragmentation strategy and the service requirement, the calculation push-down of the query process and the result optimization are realized, and the low delay and the high availability in a high-concurrency scene are ensured.

The invention provides a financial machine room AI operation and maintenance method and device and a readable storage medium. The method comprises the steps of obtaining multi-modal data; preprocessing the multi-modal data to obtain preprocessed data; transform depth feature fusion is carried out on the preprocessed data, and a fusion feature vector is obtained; according to the fusion feature vector, LSTM time sequence prediction and evaluation are carried out, a prediction result is obtained, and the prediction result comprises at least one of the equipment health degree score, the fault occurrence probability, the business influence degree and the residual service life of the equipment. According to the application, the physical state monitoring of the equipment and the real-time state of the financial service are deeply fused, so that the fundamental transformation from the traditional passive response to the active prevention is realized. According to the invention, a business-aware intelligent prediction model and a transaction-aware-free resource scheduling mechanism are constructed especially for the high availability requirement and the transaction continuity guarantee requirement of a financial machine room.

The invention discloses a real-time data subscription method and device based on a time sequence database and a medium, belongs to the technical field of databases, and aims to solve the technical problems that the real-time performance is poor, the resource consumption is large and the time sequence data required by the current time sequence database in real time acquisition in mass data storage and processing is poor. According to the technical scheme, the method comprises the steps that a data subscription scheduling task is constructed through a predefined SQL statement, in combination with an efficient data change capture module based on a Raft log mechanism, data subscribed by a user is obtained in real time, and the data subscribed by the user is filtered according to a customization rule; then the screened data is automatically transmitted to a preset target sink component in a specified format, so that the whole data processing flow has the characteristics of real-time performance, accuracy and high availability; the method specifically comprises the following steps: constructing a data subscription scheduling task; executing the data subscription scheduling task; and capturing data change based on the raft log.

The invention provides a data driving and multi-element cloud edge cooperative computing method for intelligent workshop construction, which comprises the functions of multi-protocol heterogeneous equipment sensing, edge node adaptive load balancing, lightweight real-time edge computing processing, cloud centralized management control, industrial intelligent application service deployment scheduling, cloud edge task unloading and the like. The system comprises a data acquisition and transmission middleware, an edge stream processing engine and a cloud service center. The method has the characteristics of real-time performance, low delay, flexibility, expandability, loose coupling, low resource occupancy, high availability and privacy security.

The invention discloses a programmable logic controller programming system with an internet of things function, which relates to the technical field of industrial automation control and comprises a main control module based on a heterogeneous multi-core processor architecture and used for executing user program logic and coordinating operation of a communication module, a task distribution module, a dynamic loading module and a safety verification unit; according to the programmable logic controller (PLC) programming system of the internet of things, the real-time performance and the resource efficiency of an industrial control system are remarkably improved through heterogeneous multi-core collaboration, dynamic resource scheduling and multi-protocol compatibility technologies; on the basis of differential division of labor of the ARM and the FPGA, master core global scheduling and slave core high-real-time control are combined, and millisecond-level response of key tasks is guaranteed; compared with a traditional PLC, the system breaks through the limits of protocol closure, resource rigid distribution, shutdown dependence and the like, provides a flexible, stable and safe intelligent control core for the industrial Internet of Things, and promotes industrial automation to evolve towards high efficiency, networking and high availability.

A Network Virtualization Device (NVD) executes a set of Virtual Network Interface Cards (VNICs). The set of VNICs includes a first VNIC that forwards packets for a set of one or more packet flows. The NVD stores a first VNIC-related information that includes information identifying a first set of one or more packet flows and associated state information The NVD in response to determining that the state information for the first VNIC is to be synchronized with another NVD, identifies a first backup NVD for the first VNIC, wherein the first backup NVD is a backup for the first VNIC, and communicates to the first backup NVD, a portion of the state information stored by the NVD for the first VNIC.

The invention relates to a cross-domain exchange engine equipment load balancing method based on dynamic consistency hash, and belongs to the technical field of load balancing. According to the method, a dynamic concept is introduced to enable a consistency Hash algorithm based on virtual nodes to adapt to a software and hardware load environment of cross-domain switching engine equipment, and key parameters such as a CPU utilization rate, a memory utilization rate, a disk IO rate, a network bandwidth occupancy rate and network time delay of the cross-domain switching engine equipment are comprehensively considered to reflect a current service load condition of the cross-domain switching engine equipment; therefore, requests are dynamically allocated to cross-domain exchange engine equipment on a hash ring, load balancing is achieved, request delay of a shared exchange system is effectively reduced, the system has higher availability, and the service capacity and the resource utilization rate of the system are effectively improved.

There are provided systems and methods for dynamic creation of data specification-driven AI-based executable strategies for high availability of evaluation services. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and artificial intelligence models for decision-making of data including data in production computing environment. A decision service may normally be used for data processing and decision-making. However, at certain times, the decision service may fail or the services and / or a gateway for such services may be inaccessible. To provide higher availability and better SLA times, a client-side executable strategy for decision service execution may be determined using the pathways for strategy execution and available data from called resources. This strategy may be loaded in parallel to calling the decision service, and when failure occurs, may be used as a fallback to request processing.

The invention provides a multidimensional predictable load balancing fault switching method and a multidimensional predictable load balancing fault switching system. The method comprises the following steps: constructing a machine learning model based on time sequence data of historical fault probabilities, and predicting the fault probabilities of fault granularities in a preset period of time in the future by adopting the machine learning model; and when the predicted fault probability exceeds a corresponding standard threshold value, triggering a preheating mechanism for disaster recovery switching, including operations of hot standby starting, state synchronization and standby cluster traffic undertaking. According to the technical scheme, through the multi-dimensional prediction and flexible switching mechanism, various faults in the load balancing system can be sensed and dealt with more timely and more accurately, the high availability and reliability of the load balancing system are remarkably improved, and stable operation of services and continuity of user experience are ensured.

The invention discloses a test method, device and equipment and a computer readable storage medium, which are applied to the technical field of computers, and comprise the following steps: obtaining multi-source data of a distributed storage cluster, the multi-source data comprising hardware state data, service associated operation data and business dependence data; constructing a relation dependency graph among the hardware nodes, the service nodes and the service nodes based on the multi-source data; adjusting the chaos test script according to the real-time state of the distributed storage cluster and the dynamic adjustment strategy to obtain a target chaos test script; and executing the target chaos test script, determining a fault range and a recovery mechanism based on the relation dependency graph, and outputting a test report. According to the method, comprehensive and accurate presentation of the dependency relationship of each node is realized, and the chaos test script can adapt to the dynamic change of the cluster, so that the test scene is more practical, the invalid test is avoided, the effectiveness, reliability and efficiency of the cluster chaos test are remarkably improved, and the high availability and service continuity of the cluster are powerfully guaranteed.

A system is disclosed for providing Open RAN CU-UP high availability, the system comprising: at least one active CU-CP; at least one active CU-UP in communication with the at least one active CU-CP; and at least one standby CU-UP in communication with the at least one active CU-CP; wherein when a message may be received from a CU-CP that detects a failure of the at least one active CU-UP, the at least one standby CU-UP may be configured to take over and become an active CU-UP, thereby providing failover redundancy for the at least one active CU-UP.

The invention relates to the technical field of cloud computing, in particular to a cross-domain PaaS cloud platform distributed architecture scheduling method and system. The method comprises the following steps: carrying out architecture design and deployment, deploying a main node, a standby node and an arbitration node in different data centers, and configuring network connection; carrying out load balancing configuration, setting a load balancer and a health check mechanism, and regularly detecting the state of a service node; designing a scheduling strategy, and designing a scheduling algorithm according to the node load condition, the resource utilization rate and the response time; and carrying out distributed database deployment and formulating an availability strategy, including configuring an arbitration node and designing a database availability strategy. High availability of the system is achieved through multi-data center deployment, the overall performance of the system is improved through intelligent load balancing and dynamic resource scheduling, data consistency and service continuity are ensured through an arbitration mechanism, the technical problems of a traditional PaaS architecture in the aspects of cross-region deployment, resource scheduling and disaster recovery are solved, and the system is suitable for large-scale popularization and application. And cross-domain multi-activity, intelligent scheduling and automatic disaster recovery are realized.

The present disclosure includes systems and methods for symmetric routing and split-brain handling in high-availability (HA) networks using route priority and route affinity inversion. In one aspect, the method includes receiving, at a controller associated with a communication network, first status information associated with at least one of a first node or a second node. The first node and the second node are used in service of a first VPN. The controller determines, from the first status information, a preference associated with the first node over the second node for servicing traffic of the first VPN, and generates routing information for a third node of the communication network. The routing information specifies that the first node is preferred for serving traffic of the first VPN, and that the second node is available, but less preferred for servicing traffic of the first VPN.