762 results about "High availability" patented technology

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 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.

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 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.

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.

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 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 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.

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.

Techniques are disclosed for computing a priority of routes advertised by nodes implementing Layer-3 (L3) Multi-Node High Availability (MNHA) for a Software-Defined Wide Area Network (SD-WAN) interconnecting a first network device and a second network device according to link adherence to performance requirements. In one example, a node computes a priority for a route to the second network device based at least in part on a comparison between (1) one or more performance measurements of a link between the node and the second network device and (2) one or more performance requirements for the link. In some examples, the computed priority for the route is further based in part on a preference for the node. The node exports, to the first network device, the route to the second network device, wherein the route comprises data specifying the computed priority for the route.

The invention discloses a key value storage system indexing method oriented to an NVM-NVMe SSD (Non-Volatile Memory-Non-Volatile Memory Express Solid State Disk) hybrid architecture, which comprises the following steps of: constructing a heterogeneous storage architecture taking cold and hot data perception as a core driving mechanism, coordinating and managing two types of storage media, namely a non-volatile memory NVM and a solid state disk NVMe SSD, and realizing efficient identification, layered writing and dynamic migration of cold and hot data. According to the method, the characteristics of low delay, durability and byte addressing of the NVM are utilized, the frequency and I / O overhead of Flush and Compaction operations are reduced in a data write-in path, meanwhile, the problem of mixed storage of cold and hot data is avoided, and therefore the overall performance and storage efficiency of a system are remarkably improved. In addition, by introducing an asynchronous migration module, the method can dynamically adapt to the change of data popularity along with time evolution, and effectively support high performance and high availability of the key value system in long-term operation.

The invention provides a data center interconnection link fault self-healing and path switching method, which comprises the following steps of: acquiring multi-dimensional link performance monitoring data, establishing a standardized time sequence database, realizing link health degree multi-index trend prediction by combining an LSTM-Attention model, and determining the link health degree according to the LSTM-Attention model. Risk-aware dynamic path selection and switching for multiple business scenes are realized by combining business service sensitivity modeling, path inherent stability quantification, nonlinear adaptive switching criteria and a debounce switching process, the response accuracy and the business adaptation degree of path switching are improved, false triggering and service interruption risks can be remarkably reduced, and the path switching efficiency is improved. And the high availability and robustness of routing in a complex network environment are enhanced.

The invention provides an AI model intelligent training and reasoning integration method and system, and the method comprises the following steps: receiving a model training instruction, and carrying out the preprocessing of original data, and obtaining a training data set; executing model training based on the training data set, monitoring task priorities and resource requirements through a dynamic resource scheduling algorithm, and dynamically adjusting training resource allocation according to a real-time monitoring result; when the model reaches a preset performance index, performing model pruning and quantification to generate an optimization model, and performing parameter fine tuning on the optimization model to obtain a final deployment model; generating reasoning service configuration according to calculation characteristics of the final deployment model, migrating the model and the configuration to a reasoning environment, and starting reasoning service; and dynamically adjusting the number of reasoning nodes according to the real-time network flow of the reasoning service. By implementing the technical scheme provided by the invention, through bidirectional dynamic resource scheduling and model deep optimization, the computing resource utilization rate and the model deployment efficiency are improved, and the high availability of the reasoning service is guaranteed.

The invention relates to the cross technical field of a distributed artificial intelligence system and micro-service governance, in particular to a service monitoring and management system based on a distributed Agent, which adopts a three-level architecture of a user layer, a host Agent layer and a service Agent layer: the user layer is provided with a unified entrance forwarding request; the host Agent layer is integrated with a service registry and other modules to realize global management and control; the service Agent layer comprises a registration module and the like to complete function and state feedback. Through four innovations of a dynamic observability model, an incremental heartbeat protocol, a fine-grained state machine and an intelligent routing engine, dynamic binding of a service function and a state is realized. The scheme has the advantages that the service observability is improved, and the problem positioning efficiency is optimized; the monitoring data transmission quantity is reduced; the service interruption time is shortened, and the availability is improved; and the method is suitable for scenes such as intelligent customer service and the like.

The invention discloses an intelligent database switching method and device, computer equipment and a storage medium, belongs to the technical field of big data, and is applied to a financial database downtime processing scene. According to the method, the node state of the database is monitored in real time, multi-dimensional load evaluation is combined, the optimal main and standby database combination is automatically selected, second-level fault sensing and rapid switching are achieved, and a minute-level time window needed by traditional database recovery is remarkably shortened. The database identifier is embedded in the service main key, so that the request can accurately position the corresponding database node, complex cross-database query and routing overhead are avoided, and the system response efficiency is improved. In addition, through data seamless transmission and automatic abnormal switching between the main database and the standby database, the risk of service interruption caused by database faults is reduced. According to the scheme, the high availability and the fault recovery speed of the database are improved, repeated construction of a multi-product-line database cluster is reduced through resource integration, and a large amount of hardware and operation and maintenance cost are saved.

The invention belongs to the technical field of network management, and particularly relates to a road toll collection system network security task dynamic allocation and load balancing method, which comprises a road toll collection system, and the road toll collection system comprises a network security task dynamic allocation system and a load balancing system. The network security task dynamic allocation system serves as an intelligent security protection scheduling center, is used for coping with continuous evolution threats, and comprises a real-time threat perception and response scheduling integration, a resource elastic scaling system and a strategy self-adaptive adjustment system. The load balancing system serves as a foundation stone of service continuity and performance, ensures stable operation of high-concurrency transactions, and comprises an intelligent flow distribution system, an automatic fault isolation and recovery system and a load balancing system. The method can deal with sudden security events, optimize the resource utilization rate, improve the protection precision, guarantee high availability, improve the user experience and support the elastic expansion of the system.

The invention provides a simulation deduction method and system based on distributed parallel scheduling and a storage medium, and is applied to the technical field of data processing.The method comprises the steps that proxy services are deployed at a plurality of preset computing nodes, resource state information of all the nodes is dynamically collected, and a computing resource pool is obtained; arranging a pre-registered plug-in based on the data dependency relationship to obtain a simulation task process; in response to simulation scene configuration submitted by a user, generating a distributed scheduling task containing a fragmentation strategy; determining a plurality of sub-tasks corresponding to the distributed scheduling task based on the simulation task process; and based on the real-time load of the computing resource pool and the task priorities of the plurality of sub-tasks, respectively distributing the plurality of sub-tasks to a plurality of target computing nodes for task execution, and obtaining task execution result information. According to the method and the device, the time delay performance of distributed scheduling and the high availability performance of scheduling simulation tasks can be improved.

The invention relates to the technical field of distributed storage, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data storage method, device, equipment and medium based on partition identifier mapping, and the method comprises the following steps: receiving a file and dividing the file into data blocks to generate block identifiers; generating a partition identifier based on the file identifier and the block identifier through hash mapping; creating a partition disk pack mapping table and writing an initial relationship; querying the mapping table to obtain a disk group, and writing the data block into a physical disk to generate a copy; monitoring the health of the disk, keeping the partition identifier and the block identifier unchanged when a fault occurs, and updating the mapping relation to a new disk group; and receiving a read-write request of the target partition identifier, querying the mapping table to obtain the target disk pack, and executing access. According to the method, the partition identification and the block identification are kept unchanged, fast fault tolerance is achieved only by updating the mapping relation, metadata updating expenditure is reduced, bottom layer change is shielded through partition identification routing, and access transparency and high availability are achieved.

The invention discloses a collaborative task scheduling method supporting a multi-region computing power host, and relates to a cloud computing and edge computing technology. Comprising the steps of 1, constructing a resource awareness and dynamic portrait, 2, making a collaborative decision of task scheduling, and 3, performing data pre-distribution and storage optimization. 4, managing load balance and elastically expanding and shrinking capacity: formulating a dynamic load migration mechanism and performing elastic resource expansion, and 5, performing fault-tolerant and high-availability management: deploying dual-active node redundancy, performing breakpoint resume and state snapshot, and balancing storage overhead and recovery efficiency.

The invention provides a business system development method and device and a storage medium, and the method comprises the steps: in the business system development method, obtaining a visual configuration page based on a front-end low-code framework, configuring a data verification rule corresponding to the visual configuration page, and generating an initial business system based on the configured visual configuration page; and performing data synchronization processing on each service module in the initial service system, and generating a target service system, thereby realizing online development of the service system, improving the development efficiency through low-code development, reducing the development threshold, solving the problem of difficult updating of a mobile terminal through non-release updating, performing data synchronization on each service module, and improving the user experience. Therefore, integration of service modules of distributed transactions is realized, data consistency and high availability of the generated target service system in a distributed environment are ensured, and the development efficiency of the service system is improved.

The invention discloses a clock synchronization method and device, electronic equipment and a medium, and relates to the technical field of communication. And then one or more clock maintenance nodes can be determined by using the time delay between the target node corresponding to the global clock information and each other node except the target node. And if the target node or the clock maintenance node receives the clock synchronization request of the to-be-synchronized node, responding to the clock synchronization request, and pushing the global clock information to the to-be-synchronized node. Both the target node and the clock maintenance node in the distributed system have the global clock information, so that high availability of the global clock information in the distributed system is ensured, and the problem of unreliability caused by clock synchronization depending on a single node in the distributed system is solved.

The invention discloses a failover method and device, electronic equipment and a storage medium, and relates to the technical field of distributed storage, and the failover method comprises the following steps: determining a first disk resource group corresponding to a first storage node and a second disk resource group corresponding to a second storage node; and establishing a double-control relationship between the first disk resource group and the second disk resource group. And after the establishment of the double-control relationship is completed, carrying out fault switching on different disk resource groups under the double-control relationship based on the first mechanism and the second mechanism. The technical problem that in a distributed storage system, efficient and safe disk resource management and data access control of a fault controller cannot be effectively achieved under a double-controller architecture is solved, and the technical effects of improving rapidness, smoothness and automatic back-switching of fault switching and remarkably improving the high availability of the distributed storage system are achieved.

The invention relates to the technical field of computers, and discloses a document collaboration platform based on cloud computing. Real-time semantic complementation, automatic typesetting optimization and content abstract generation are provided through the intelligent editing assistant module, the manual editing burden is remarkably reduced, and the efficiency is improved; a rule engine of an automatic operation module is combined to realize automatic management of document versions, intelligent resolution of conflicts and standardized review, and the problems of communication cost and conflicts in multi-person cooperation are thoroughly solved; the problem of information overload is solved by relying on the deep semantic processing capability, and accurate positioning of core content is realized; and meanwhile, based on the elastic expansion capability of the distributed cloud architecture, the stability and high availability of multi-terminal real-time cooperation are ensured. Finally, a document processing closed loop integrating intelligent assistance, an automatic process and efficient cooperation is formed, and the bottlenecks of a traditional platform in the aspects of editing efficiency, cooperation fluency, information processing and the automation level are comprehensively broken through.

The invention relates to a graph database storage and management method, and the method comprises the steps: determining a cluster deployment strategy based on a cluster creation instruction, creating a corresponding Zone according to the cluster deployment strategy, and determining the number of copies of each Zone; according to the number of copies of each Zone and the total number of fragments configured by the graph database, distributing fragment copies on a physical machine of each Zone in a polling manner to obtain a fragment distribution scheme, and creating corresponding fragment copies on the physical machine according to the fragment distribution scheme; and in response to a query instruction initiated by a client, obtaining a routing strategy and the latest fragmentation distribution view information from the management node, and accessing a storage engine of the Zone by a query engine in each Zone based on the routing strategy and the fragmentation distribution view information. Through the method and the device, the problem of low availability when the graph database deploys the service is solved. The graph database service is deployed based on the Zone, and the granularity of the Zone can be self-defined, so that the cross-rack / machine room level and cross-region level high availability is realized.

The invention provides a self-healing graph scheduling system and method for a multi-agent system. According to the method, in the multi-agent task flow graph, when any node fails or needs to be upgraded, bypass or hot replacement can be automatically completed within the time lower than a preset failure threshold value, and it is ensured that task topology is continuously acyclic, data are not lost, and services are not interrupted. According to the method, in a directed acyclic graph of a multi-agent task process, a main / standby node is configured for each edge, and the health state of the nodes is monitored in real time through dual-channel Gossip heartbeat; when the main node meets the failure condition, the flow is automatically redirected to the backup node at the millisecond level, the node state is recovered by using the XOR-delta snapshot, and the topology acyclic property is verified at the same time, so that the task is ensured to be continuous and traceable. Experimental results show that the average recovery time is reduced to 18 ms, the annual downtime is reduced by more than ten times, and the method is suitable for intelligent finance, industrial internet, automatic driving and other real-time scenes needing parallel agent collaboration and high availability.

The invention provides a data processing method and system based on network security service, relates to the technical field of network data security processing, and realizes structured expression and behavior extraction of potential threats in encrypted communication by establishing a new data structure and behavior association model. By introducing a context causal chain modeling mechanism, associating the originally isolated security events into a complete attack path; by establishing a set of event grading mechanism based on comprehensive scoring of service criticality, response cost and attack propagation path, threat processing does not distribute resources blindly and averagely, but performs intelligent scheduling according to priority, so that the resource utilization efficiency is improved; a structured and executable defense instruction or blocking strategy can be generated according to an analysis result, man-machine cooperation or full-automatic response is supported, and response efficiency and strategy adaptability are remarkably improved. And a set of intelligent data processing solution which is oriented to a network security service practical application scene and has high availability and high expansibility is constructed.

Techniques are described for enabling a container service of a cloud provider network to detect imbalances of container placements across a selected set of availability zones (AZs) and to automatically rebalance placement of the containers, if needed. An actual distribution of containers may differ from an expected distribution according to a configured placement strategy, e.g., due to an outage or other operational issue affecting one or more of the AZs, scaling operations over time, and the like. In these and other scenarios, the container service can rebalance placement of the containers by terminating one or more containers in one or more of the AZs and launching additional containers in one or more other AZs to restore a more desirable balance. The periodic rebalancing of containers in this manner improves a container-based application's ability to load balance demand and further improves application availability by ensuring sufficient capacity is spread across multiple AZs.