3272results about "Redundant hardware error correction" patented technology

The invention discloses a server cooperative control method, a storage medium and electronic equipment, and relates to the technical field of firmware, and the method comprises the steps: employing a programmable logic device to actively take over the control authority of target equipment based on the operation parameters of a sensing device in a server starting stage, stable operation of equipment before initialization of the substrate management control device is not completed is ensured, a mechanism that a preset communication link and the substrate management control device are subjected to interactive verification is adopted, and the functional integrity and communication reliability of the substrate management control device during permission transfer are ensured through bidirectional communication verification. A dynamic permission recovery strategy based on real-time anomaly detection is adopted, control right reverse switching is triggered through a sensing device and communication link state dual monitoring mechanism, and the purpose of actively taking over equipment management responsibilities when a substrate management control device runs abnormally is achieved. Therefore, double improvement of system fault self-healing capability and equipment control continuity is realized, and the problem of difficulty in cooperative control among heterogeneous units is solved.

The invention relates to the technical field of server temperature control, and discloses a liquid cooling server intelligent temperature control method based on local software monitoring, and the method comprises the following steps: data collection: collecting hardware temperature data and task load data of a liquid cooling server through an embedded real-time monitoring module; temperature prediction: based on the hardware temperature data and the task load data, utilizing a prediction algorithm to analyze the temperature change trend of the server in a period of time in the future; regulation and control decision making: according to the temperature change trend, generating regulation and control parameters for regulating the flow of the cooling liquid and the target temperature of the cooling liquid; the invention further discloses a liquid cooling server which comprises an embedded real-time monitoring module, a distributed control unit, a cooling loop module and a local off-line temperature control module. Through the dynamic temperature control algorithm, the distributed control unit and the local temperature control module, real-time accurate regulation and control, efficient response and independent operation under the condition of network disconnection are achieved, and the reliability and adaptability of the system are improved.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic signal edge computing resource dynamic allocation method and system.The method comprises the steps that firstly, real-time and historical traffic data of multiple intersections are obtained, a traffic prediction model is constructed, and future flow changes are pre-judged; thirdly, forming an edge computing resource demand matrix according to a prediction result and computing resource demands, and dynamically adjusting resource allocation to generate a resource slicing scheme; then, the flow gradient between intersections is analyzed, an edge node cooperative calculation model is established, and calculation task distribution and migration are optimized; and finally, in combination with system operation and traffic control effects, forming closed-loop feedback, continuously optimizing resource allocation and a cooperative strategy, and through traffic flow prediction driven resource demand modeling, realizing active pre-allocation of computing resources, converting resource allocation from passive response to active pre-adaptation, and improving the system resource utilization rate by 40%-60%.

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 discloses an IT asset fault propagation prediction method and system based on dynamic evolution of a knowledge graph, and relates to the technical field of cloud computing and large-scale IT operation and maintenance management. Through an asynchronous message bus and a logic clock, the knowledge graph is updated immediately when resources are abnormal and a scheduling event occurs; the knowledge graph uniformly integrates physical connection, logic dependence and multi-copy redundancy, so that the cross-machine-room asset relationship is clear at a glance. And then, based on a weighted logistic regression model, node features and relation weights in the knowledge graph are fused, the node fault probability is accurately calculated, the limitation of traditional single-dimensional analysis is solved, self-healing operation is supported, end-to-end intelligent operation and maintenance from fault detection to prediction and early warning to closed-loop self-healing are realized, and the fault detection efficiency is improved. The problems that in a cross-machine-room and multi-live-site environment, resource topology is split, real-time state and alarm information cannot be fused with an asset dependence model, and large-scale real-time deployment of a traditional single-dimensional fault analysis and high-complexity prediction algorithm is difficult are effectively solved.

The invention discloses a data reconstruction method and device, a storage medium and a program product, and relates to the technical field of data storage, and the method comprises the following steps: reconstructing an initial redundant array of independent disks according to a hard disk state of the initial redundant array of independent disks to obtain a target redundant array of independent disks; after the reconstruction of the initial redundant array of independent disks is completed, establishing a mapping relation between the initial redundant array of independent disks and the target redundant array of independent disks, and storing the mapping relation based on metadata to obtain corresponding target metadata, and recovering the target redundant array of independent disks to the initial redundant array of independent disks according to the target metadata. When the disk fails or restores to normal for data reconstruction, the corresponding data can be directly written back according to the mapping condition between the blocks, additional check block calculation is not needed, and the data recovery speed is increased.

A system for dynamically scaling microservices in cloud environments, including: a predictive scaling module that uses machine learning algorithms to analyze historical and real-time workload data and predict resource requirements; an auto-scaling decision engine configured to determine and execute dynamic scaling actions based on predictions and current system performance; a cloud orchestration module that deploys and manages microservice instances in cloud environments to ensure optimal resource allocation; an intelligent load balancer that dynamically redistributes incoming requests across microservice instances to avoid bottlenecks and maintain service reliability; and a real-time monitoring module configured to continuously collect system performance data, detect workload fluctuations, and provide feedback for adaptive scaling.

The invention discloses a heterogeneous computing multi-target adaptive task scheduling method based on deep reinforcement learning, and the method comprises the following steps: S1, constructing a multi-dimensional dynamic perception model of a heterogeneous computing environment, and collecting and computing node performance indexes, task feature parameters and network states in real time; s2, defining a reward function as a multi-target weighted combination, fusing task completion time, energy consumption, resource utilization rate and cost, and dynamically adjusting the weight by a fuzzy comprehensive evaluation algorithm; s3, establishing a dual-channel deep reinforcement learning network architecture based on an attention mechanism; s4, establishing an adaptive exploration mechanism, combining an epsilon-greedy strategy and entropy regularization, and balancing exploration and utilization; the method has the beneficial effects that dynamic balance of multiple indexes such as task completion time, energy consumption and resource utilization rate is realized through combination of deep reinforcement learning and multi-objective optimization, a dual-channel network and a cross attention mechanism are adopted, and a task time sequence characteristic and a topological dependency relationship are modeled at the same time, so that a scheduling strategy is more accurate.

The invention relates to the technical field of distributed simulation based on containerized deployment and elastic expansion and contraction, and discloses a distributed simulation method and system based on containerized deployment and elastic expansion and contraction. According to the distributed simulation method and system based on containerized deployment and elastic expansion, real-time resource monitoring and historical load trend data of a simulation task are collected, and unified load evaluation is carried out in combination with a simulation calculation complexity parameter and an I / O density parameter; the refined modeling and resource demand pre-judgment of the simulation task are realized, and the accuracy of task scheduling and allocation is improved; modularized deployment of simulation tasks is achieved through subtask segmentation based on minimum executable units and a standard containerization packaging mechanism in cooperation with a container arrangement platform, and then concurrent execution and elastic scheduling in a multi-node environment are supported.

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 relates to a reservoir dam operation safety sky-land project intelligent sensing system and an operation method, and relates to the technical field of hydraulic engineering safety monitoring. The system is composed of a sky-land water conservancy project integrated monitoring and sensing system, a self-adaptive sampling module, a layered distributed architecture and a software and hardware integrated module, and multi-source data such as deformation, seepage, stress strain, vibration and environmental quantity are cooperatively collected through five dimensions of sky domain, airspace, territory, water domain and work domain. The monitoring frequency is dynamically adjusted by using an adaptive sampling strategy, and data cleaning, standardization, space-time registration and fusion processing are completed through a distributed architecture to generate a high-quality comprehensive data set. The system can realize total-factor and whole-process refined monitoring, effectively eliminates data islands, improves data quality and monitoring efficiency, has high reliability, real-time performance and expandability, and provides powerful data support and decision basis for dam safety assessment and intelligent early warning.

The invention discloses a full-stack NPU system supporting a multistage fault mitigation mechanism, and the system comprises a plurality of NPU functional blocks which are used for executing a neural network calculation task; the plurality of hardware protection modules are used for providing error detection and correction capabilities for at least one NPU functional block in the NPU or a data path in the NPU; and a control mechanism for coordinating operation of the plurality of hardware protection modules. By means of the scheme, NPU functional block calculation errors or data path transmission errors caused by hardware faults and the like can be detected and corrected in time, wrong calculation results are prevented from being continuously used or output, and the accuracy of final results is guaranteed. Meanwhile, even if part of hardware breaks down, an error correction mechanism can also cover errors to a large extent, normal operation of the system is maintained, the overall availability and task success rate of the system are improved, and therefore the reliability and robustness of the NPU are remarkably improved.

Techniques are described for efficient replication and maintaining snapshot data consistency during file storage replication between file systems in different cloud infrastructure regions. In certain embodiments, provenance IDs are used to efficiently identify a starting point (e.g., a base snapshot) for a cross-region replication process, conserve cloud resources while reducing network and IO traffic.

Techniques are described for efficient replication and maintaining snapshot data consistency during file storage replication between file systems in different cloud infrastructure regions. In certain embodiments, snapshot creation and deletion requests that occur during cross-region replications may be temporarily withheld until appropriate times to execute such requests safely, depending on the timing relationship between such requests and cross-region replication cycles.

The invention belongs to the field of high-performance computing, and relates to an integer parallel computing method and device based on distributed storage and computer equipment, and the method comprises the steps of collecting real-time resource indexes, dynamically identifying fault nodes, triggering task migration, and performing data verification and hard disk fault detection. The weight value of each node is calculated, the nodes are arranged according to the descending order of the weight values, and the nodes with high load capacity are selected to distribute tasks; dynamically distributing a data generation task to a computing node, executing parallel computing, and performing distributed storage on a result; obtaining an operand, converting the operand into a first-order tensor form of a basic operand, serializing tensor data, and sending the serialized tensor data to a parallel computing layer; distributing a search task to a computing node, retrieving storage data in parallel, reading effective data from a storage layer, and combining search results into a partial sum; and summarizing and then outputting. The system has dynamic resource management and fault-tolerant capabilities, and can realize efficient task allocation and load balancing.

Techniques for recovering data involve, in response to detecting that a data block of a storage system is attacked, determining one or more attacked data blocks, wherein snapshots in multiple snapshots of the storage system have snapshot replicate numbers. Such techniques further involve determining one or more data block replicate numbers of the one or more attacked data blocks. Such techniques further involve identifying the latest target snapshot in the multiple snapshots based on the snapshot replicate numbers of snapshots in the multiple snapshots and the one or more data block replicate numbers, wherein all data blocks in the target snapshot are not attacked. Such techniques further involve recovering data of the storage system based on the latest target snapshot.

The invention discloses an AI server data processing optimization system and method based on distributed heterogeneous computing, and relates to the technical field of artificial intelligence and distributed heterogeneous computing, the AI server data processing optimization system comprises a distributed heterogeneous computing cluster, a dynamic collaborative scheduling subsystem and a double-layer closed loop verification subsystem; the distributed heterogeneous computing cluster is characterized in that the distributed heterogeneous computing cluster comprises a plurality of computing nodes which are interconnected, and each node integrates at least two hardware units. According to the AI server data processing optimization system and method based on distributed heterogeneous computing, deep collaborative optimization of computing resource scheduling and data stream transmission is achieved by constructing a hardware-data-task ternary graph model and a space-time collaborative propagation algorithm, so that end-to-end processing delay is shortened, and the heterogeneous resource utilization rate is maximized.

The invention discloses a distributed computing system and a fault processing method for the distributed computing system, and relates to the technical field of computers. In the distributed computing system provided by the invention, the computing node group and the backup node are accessed to the switching equipment, so that when a management node of the distributed computing system detects that a fault occurs in the computing node group, the switching equipment can be configured to establish a data switching path between the backup node and the computing node group; therefore, the backup node can exchange data with the computing node group through the data exchange path so as to replace the faulty equipment in the computing node group to execute the computing task, and the fault processing cost is greatly reduced on the basis of maintaining the original performance of the system and the network topology.

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.

A control execution unit is connected both a main bus and a sub bus, and includes an execution unit and a selection unit. The execution unit performs vehicle control according to a selected manipulated variable being either a main manipulated variable from a main processing unit connected to the main bus or a sub manipulated variable from a sub processing unit connected to the sub bus. The selected manipulated variable is set to the main manipulated variable in an initial state, and the selection unit switches over the selected manipulated variable from the main manipulated variable to the sub manipulated variable when communication performed via the main bus satisfies a preset switchover condition.

The invention relates to the technical field of project management, in particular to a large-model batch reasoning and data flow optimization system oriented to an MOE architecture. According to the method, a collaborative architecture of the request access module, the environment sensing module, the expert routing engine, the resource scheduling module and the dynamic optimization control module is set, the text length and the subject type are extracted by using the request access module, a basis is provided for accurate routing, and the GPU video memory, the I / O bandwidth and the request queue depth are acquired in real time through the environment sensing module, so that the real-time routing is realized. The system load is comprehensively monitored, meanwhile, an expert sub-network is activated through an expert routing engine according to request features, invalid calculation is avoided, weight loading and resource allocation are managed through a resource scheduling module, the I / O bottleneck is reduced, and finally an optimization strategy is intelligently triggered through a dynamic optimization control module based on routing conflict factors. The problems of large reasoning delay fluctuation and unbalanced resource utilization rate mentioned in the background technology are solved, and stable low-delay response and resource collaborative optimization in a high-concurrency scene is realized.

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 provides an operation management method of a multi-core particle computing system. The method comprises the steps that a chip of the multi-core particle computing system comprises a general processor and a plurality of functional core particles; the universal processor distributes tasks to each functional core particle, and the functional core particles report the working state and the current working site to the universal processor; the universal processor judges whether the functional core particles are normal or not according to the working state, and if yes, the current working site corresponding to the functional core particles is stored; and otherwise, judging that the functional core particle has a fault, distributing the task currently executed by the functional core particle with the fault to the functional core particle with the same function and normal working state, and restarting the task distributed to the functional core particle with the fault from the stored working site by the functional core particle with the normal working state to obtain a task execution result.

The invention discloses a cloud-side collaborative resource management system supporting edge cluster autonomous governance, and relates to the field of cloud-side collaborative edge computing, and the system comprises a cloud-side collaborative resource management framework which comprises an edge autonomous decision module. Tasks are autonomously allocated based on a local candidate list, and an emergency metadata synchronization mode is started; the cloud edge collaborative resource management framework further comprises a load collection module; a management unit; and a Web back-end module. According to the invention, node load data is collected to evaluate the idle degree by using a setting mode that the load collection module, the node role management module, the metadata management module, the Web back-end module and the task management module are matched, the information of the most idle node is sent to the node role management module to update candidates, the most idle node is selected as a leader, the overlarge load is avoided, and the task management efficiency is improved. Even if the cloud node is abnormal, the edge cluster can still operate stably and efficiently, and a solid support is provided for various services.

The invention relates to the technical field of hardware fault early warning, in particular to a server hardware fault early warning and recovery method and system based on an intelligent optimization algorithm, and the method comprises the steps: obtaining hardware self-inspection information; acquiring load state information; acquiring historical operation data; constructing a preliminary mapping matrix based on the acquired hardware self-inspection information, load state information and historical operation data; according to the preliminary mapping matrix, through a topological space optimization algorithm, key modes are extracted, and an optimized mode matrix is generated; based on the optimized mode matrix, combining a fault-tolerant strategy group, and screening a fault-tolerant or backup strategy suitable for the current state; according to a fault-tolerant or backup strategy, evaluating the benefit, the cost and the success probability of the strategy, and generating an optimal decision scheme; outputting fault early warning information including early warning time, an early warning level and a trigger strategy; and outputting an optimal decision scheme, including a fault-tolerant strategy or a backup strategy, so that a more complex hardware abnormal mode can be captured, and the accuracy of early warning is greatly improved.

In one embodiment, a system for managing a virtualization environment includes a set of host machines, each of which includes a hypervisor, virtual machines, and a virtual machine controller, and a first virtualized file server configured to receive a request to access a storage item located at a second virtualized file server, determine that the storage item is designated as being accessible by other virtualized file servers, identify an FSVM of the second virtualized file server at which the storage item is located, and forward the request to the FSVM of the second virtualized file server. The storage item may be designated as being accessible by other virtualized file servers when the storage item is associated with a predetermined tag value indicating that the storage item is shared among virtualized file servers. The predetermined tag value may be stored in a sharding map in association with the storage item.

Techniques for managing resource constraints of a cloud environment are disclosed. A system receives a request to initiate a provisioning process for provisioning a first service in the cloud environment. The system determines a resource constraint associated with a resource that the first service utilizes. Based on the resource constraint, the system determines a set of candidate services that also utilize the resource as candidates for deprovisioning from the cloud environment. The system identifies respective service features of the set of candidate services and generates a ranking of the set of candidate services based on weighting metrics associated with the respective service features. Based on the ranking, the system selects a second service of the set of candidate services for deprovisioning from the cloud environment. The system deprovisions the second service to alleviate the resource constraint and then provisions the first service by executing the provisioning process.

Techniques for fast database recovery in a multi-volume database environment via transactional awareness are described. In the event of a failure associated with a first volume storing database page data, the first volume can be restored to a point in time and transactional metadata from a second volume storing logical change data can be obtained for a limited number of transactions occurring at / after that point in time, as opposed to analyzing extremely large change log files. These transactions can be checked to ensure that they have all been persisted, and if not, change data for those transactions can be obtained from the second volume and used to replay these transactions on the restored first volume.

An elector is provided using shared storage to allow a cluster of participant nodes to gracefully convert failed voters to non-voters until there are only two voters. The cluster can then tolerate the failure of either of those two remaining participant nodes. A tiny amount of shared storage is used as a voter without an object store. This results in a cluster with only one running node that is an exclusive leader. An exclusive leader is then the only participant node that is a voter, and the exclusive leader can commit log records without replicating them to any other members of the cluster. When there is an exclusive leader, there is only one remaining node with a current copy of the object store.

A method of recommending reuse of hardware components across client information handling systems to extend hardware life may comprise receiving operational telemetries from a first and second information handling system, each including an error associated with a hardware type, and measured workloads on the hardware type, determining, based on the operational telemetries that a hardware component of the same type is failing at both the first and second information handling systems, and that the first information handling system is within a preset maximum distance from the second information handling system, determining failure of the component at the first information handling system is due to a workload on that component that is higher than the workload on the same component at the second information handling system, and transmitting a recommendation to replace the component at the second information handling system with the component from the first information handling system.