241 results about "Dynamic load balancing" patented technology

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 relates to the technical field of computer architecture and parallel computing, and discloses a parallel task scheduling algorithm for a heterogeneous multi-core processor, which comprises the steps of task modeling, resource mapping, dynamic load balancing, communication optimization, task scheduling decision and execution monitoring. Task allocation is adjusted in real time through dynamic load balancing, cross-core communication delay is reduced in combination with communication optimization, and an efficient task allocation sequence is generated by using an improved genetic algorithm. According to the method, the resource utilization rate and the task execution efficiency of the heterogeneous multi-core processor in a high-performance computing scene can be improved, meanwhile, the robustness and adaptability of an algorithm are enhanced, and the task allocation problem in a complex computing scene is effectively solved.

The invention relates to the technical field of cloud edge collaborative computing power scheduling, and discloses a heterogeneous computing power scheduling optimization method based on a cloud edge collaborative architecture. The method comprises the following steps: acquiring real-time computing power state data of all available computing nodes in the cloud edge collaborative architecture; performing heterogeneous type division on the computing nodes according to the real-time computing power state data to generate a three-layer computing power resource pool containing cloud computing nodes, edge computing nodes and terminal computing nodes; extracting task calculation features for the current to-be-scheduled task set, wherein the features comprise calculation intensity, data dependence and real-time requirements; constructing an initial task allocation scheme based on the matching relationship between the task calculation features and the three-layer computing power resource pool; iteratively optimizing the initial scheme by adopting a dynamic load balancing strategy to generate a final task scheduling instruction; the instructions are distributed to the corresponding computing nodes to be executed, and computing power state changes in the task execution process are continuously monitored.

The invention relates to the technical field of AGV intelligent scheduling, and discloses an intelligent scheduling and resource allocation method and system for a flexible AGV, and the method comprises the steps: carrying out the grid processing of an AGV operation region, and obtaining a dynamic scheduling network; modeling an AGV state space in the dynamic scheduling network to obtain a multi-agent scheduling model; calculating a task priority score of the AGV based on a multi-agent scheduling model, discretizing an operation time axis into time slices, and obtaining a time slice resource allocation result; according to the time slice resource allocation result, the AGV scheduling decision is optimized to obtain a distributed scheduling strategy; dividing the distributed scheduling strategy to obtain a master control AGV and a slave AGV, and generating a cluster collaborative decision result; and resource allocation and dynamic load balancing are carried out based on a cluster collaborative decision result to obtain a scheduling instruction of the AGV cluster, collaborative decision of the AGV cluster is realized, and consistency of global task decomposition and local resource allocation is ensured.

The invention provides a geological environment monitoring method and system, and the method comprises the steps: constructing an air-space-earth-depth four-dimensional cooperative monitoring network, collecting multi-modal geological environment data, carrying out the preprocessing of the data, and obtaining multi-modal feature data and abnormal data points after dynamic load balance distribution of calculation resources; fusing the data to generate a comprehensive geological environment feature map containing space-time correlation features, abnormal hot spot distribution and a geologic body three-dimensional reconstruction model; and based on the map, performing geological risk prediction by using a geological risk prediction model to obtain a prediction probability. A monitoring network is constructed to integrate multi-modal geological data, resource allocation is optimized by combining edge calculation dynamic load balancing, geological risk prediction is realized by using a prediction model, the problems of single data, poor dynamic adaptability and weak model generalization ability of a traditional method are solved, the prediction precision is improved, the response time is shortened, and the prediction efficiency is improved. And a high-risk area is visually displayed through a three-dimensional risk thermodynamic diagram.

The invention discloses a distributed server load balancing system based on multi-modal data fusion. The distributed server load balancing system comprises a multi-modal sensing layer, a fusion analysis layer, an intelligent decision-making layer and an elastic execution layer, the multi-modal sensing layer collects multi-source heterogeneous data; the fusion analysis layer is used for mining an association relationship between data of different dimensions; meanwhile, converting multi-source data into a unified feature vector, and outputting a dynamic load balancing strategy; the intelligent decision-making layer generates an executable load balancing strategy according to the fusion analysis result; and the elastic execution layer converts the strategy generated by the intelligent decision-making layer into an actual action, and deals with load change through an elastic mechanism. According to the method, server performance, network topology and request semantic data are fused, a unified feature tensor is generated by using a cross-modal attention mechanism, and load association is accurately quantified; on the basis of cooperation of a global mixed integer programming model and a local lightweight DQN agent, load migration is completed in a very short time, and the response speed is increased.

The invention belongs to the technical field of computers, and particularly relates to a data center server resource allocation method and system based on dynamic load balancing, and the method comprises the steps: collecting a physical resource state, instance constraint and network flow through a multi-dimensional probe, constructing a dynamic weighted load scoring model, and introducing a migration penalty term to correct node load evaluation; solving maximum weight matching of the bipartite graph with constraint by combining a Hungary algorithm, and realizing global optimal mapping of the migration instance and a target node; a memory, a CPU and network resources are pre-configured before migration, a delay recovery mechanism is triggered after migration, and the service quality is guaranteed. The system comprises a resource acquisition module, a score generation module, a matching solution module, a pre-configuration module, a self-adaptive period adjustment module and the like. The cluster resource utilization rate is obviously improved by 28%, hotspots are reduced by 76%, the SLA default rate is reduced by 92%, meanwhile, energy is saved by 15%, and collaborative optimization of efficiency and service quality is achieved.

The invention provides a CAN bus message period adjusting method and system based on dynamic load balancing, and the system measures and calculates a network load rate and an error state through a network load real-time detection module, carries out the priority grade division and parameter configuration for different messages, carries out the load partition definition according to the load rate, and carries out the adjustment of the CAN bus message period. And dynamically adjusting a message sending period according to a measurement result, adding a feedback mechanism to realize closed-loop control, and finally realizing load balancing of the system.

The invention relates to a resource allocation method and system for dynamic load balancing of a data center elastic optical network, and belongs to the technical field of communication. Comprising the following steps: initializing data center elastic optical network parameters, and generating a connection request; judging whether the residual computing power resources of the source node and the destination node meet the request requirement or not, if so, calculating K working paths, selecting the shortest path, calculating the number of required spectrum slots, and judging whether the paths have residual spectrum resources or not; if yes, layered traffic grooming evaluation parameters are calculated, and the optimal spectrum layer is selected for grooming traffic; if not, selecting the highest-price modulation format; judging whether bandwidth and optical channel resources on the path are enough, if so, allocating spectrum resources, otherwise, returning to select the next shortest path; judging whether the spectrum resources meet constraint conditions or not, if yes, allocating computing power resources, and requesting successfully; if not, returning to reselect the path; and after success, updating the network transmission path weight. According to the invention, the network resource waste and blocking rate are effectively reduced, and the optical network energy consumption is reduced.

The invention discloses an underwater acoustic processing CPU-GPU dynamic load balancing method based on a task flow model, and belongs to the technical field of underwater acoustic processing. The method comprises the following steps: deconstructing an underwater acoustic processing application into a task flow model represented by a directed acyclic graph; establishing a feature portrait including calculation complexity, parallelism and data throughput for each task node, and constructing a cost prediction model; the CPU / GPU utilization rate and the data transmission performance are monitored in real time; a processor is distributed to each task node by using a dynamic programming algorithm in combination with a cost prediction model and a real-time system state with the goal of minimizing the total task flow completion time; and dynamically scheduling tasks through a central scheduler according to a decision result, and periodically updating a strategy. According to the method, the defects that static task division lacks adaptability and neglects task dependence and communication overhead are overcome, dynamic and efficient utilization of CPU and GPU resources is achieved, and the efficiency and real-time performance of underwater sound processing are remarkably improved.

The invention discloses a dynamic load balancing method and device for an intelligent charging pile management system, and the method comprises the steps: obtaining the operation parameters of a target area transformer corresponding to the intelligent charging pile management system in a current control period, so as to obtain the real-time total load of the target area transformer in the current control period; dynamically calculating the real-time available capacity of the transformer based on the rated power and the real-time total load of the transformer in the target station area; calculating the predicted available capacity of the transformer in the next control period through a load prediction model based on the historical power data and the external environment variables; and according to the real-time available capacity and the predicted available capacity, dynamically adjusting the operation power of the plurality of accessed charging piles, and expanding the number of the accessible charging piles in a load trough period. By fusing the real-time power grid state and the multivariable predicted future load demand, the dynamic fine adjustment of the power of the charging pile and the prospective expansion of the off-peak access amount are realized, the safety of the power grid is ensured, and the capacity utilization efficiency of the transformer is remarkably improved.

The invention discloses a multi-core scheduling system and method based on interrupt affinity and a storage medium. The technical problem that data locality guarantee and load balancing are difficult to consider at the same time is solved by constructing a self-adaptive optimization closed loop of perception-decision-execution-feedback, and the method comprises the steps that a routing interruption configuration mechanism provides a basis for dynamic adjustment; an interrupt guide task placement mechanism ensures that an interrupt service program and an associated task are executed in the same CPU core, and the cache hit rate is increased; the execution overhead of an interrupt service program is brought into core load statistics through task context switching and a'task + ISR 'two-dimensional precise load evaluation mechanism triggered by ISR inlet / outlet double nodes, and real load awareness is achieved; a load-driven dynamic interrupt routing mechanism is combined with dual control of a hysteresis threshold and cooling time, so that a ping-pong effect is avoided. According to the method, dynamic load balancing is realized on the premise of keeping data locality, and the real-time response performance and throughput of the multi-core system are remarkably improved.

The invention relates to the technical field of server management and cloud computing, in particular to a dynamic load-oriented intelligent identification heterogeneous server resource expansion method. The method aims at optimizing the resource management efficiency, the hardware expansion flexibility and the overall operation stability of the system in a data center and a cloud computing environment by integrating an advanced intelligent identification technology, a real-time data analysis capability and a self-adaptive resource scheduling mechanism. The method is particularly suitable for modern data center scenes requiring high concurrent processing capability, dynamic load adaptability and high reliability, such as a large-scale cloud computing service platform, an enterprise-level distributed storage system and an artificial intelligence computing cluster.

The invention relates to the technical field of communication security, and particularly discloses a communication terminal information acquisition method and system. The method comprises the following steps: acquiring an original data stream of a communication terminal, and generating a task queue through data source marking, dynamic load balancing and protocol packaging; performing desensitization, metadata annotation and lightweight encryption on the task data to form an encrypted data block; secure transmission is realized through transmission channel selection and quality monitoring; decrypting the received data to generate a time sequence database record; generating a rule update package based on invalid data detection and machine learning analysis; and finally, outputting a standardized data set through filtering and verification. According to the invention, efficient acquisition, secure transmission and intelligent processing of mass communication data are realized, and the real-time performance and accuracy of data processing and the load balancing capability of the system are effectively improved.

The invention provides a performance test and evaluation method, system and device for a power grid credential environment and a medium, and the method comprises the steps: obtaining real-time data of each node through employing a distributed test probe and a data flow distribution strategy, and constructing a digital twinborn body of the power grid credential environment; the digital twin body comprises twin nodes corresponding to the nodes; extracting multi-dimensional performance test data of each twin node under the performance test load by adopting a classification dimension convergence method; and based on the performance test prediction data of each twin node, performing risk assessment on each twin node by adopting an entropy weight-comprehensive sorting algorithm. According to the method, dynamic load balance of each node is ensured through a data traffic distribution strategy, normal operation of the power grid credential environment is prevented from being influenced during test evaluation, and the method is suitable for a complex scene in which the power grid credential environment is easily impacted by a high load; data evaluation is carried out through performance test prediction data, data exception can be captured, and potential performance risk bottlenecks of a power grid credential environment can be found in advance.

A wireless transmit and receive unit (WTRU) may receive, from a network, first configuration information including a first resource identification (ID) and an indication of time and frequency resources to be used in ambient Internet of things (AIoT) access procedure. The WTRU may also receive first assistance information associated with the first resource ID. The WTRU may determine, based on the first configuration and first assistance information, first resource to be used to send a first AIoT message. The WTRU may send the first message and monitor the access occasions to determine if an overload occasion occurred. Upon overload detection, the WTRU may send, to the network, an overload indication. The WTRU may receive, from the network, second assistance information. The WTRU may determine, based on the first configuration and first and second assistance information, a second resource to be used to send a second AIoT message.

The invention discloses a link switching method and system under the condition of a multi-machine multi-plane complex network. The method comprises the following steps: based on the number of data transmission links and the number of available nodes, generating an optimal allocation scheme of node resources by adopting a dynamic programming algorithm, and pre-allocating a main node and a plurality of standby nodes with different priorities to each data transmission link, so that the main and standby nodes are uniformly distributed on each plant station plane; according to a preset node abnormity judgment condition and a link abnormity judgment condition, judging whether the distributed node state or the data transmission link state is abnormal or not; and when detecting that the node or the data transmission link is abnormal, automatically switching the data transmission link in combination with the current network load state based on the pre-allocated standby node priority sequence in the optimal allocation scheme. According to the invention, link dynamic load balancing, real-time state sensing and rapid fault recovery in a complex network environment are realized, and the real-time performance and stability of data transmission of a power system are improved.

The invention discloses a differential privacy-based multi-dimensional dynamic load balancing scheduling scheme (DP-DMAPSA), which is constructed on a DPDK framework and is oriented to high-concurrency scenes such as a security crowdsourcing platform and the like. According to the scheme, multi-dimensional indexes such as queue depth, CPU utilization rate and processing delay are introduced, differential privacy mechanisms such as Laplacian and Gaussian are combined, noise disturbance is performed on load data, and reasoning attacks are prevented. In scheduling, the system dynamically selects a processing core according to a weighted hash value and a privacy index to realize flow consistency and load balancing. Meanwhile, a self-adaptive sub-flow granularity adjustment mechanism is designed, dynamic adjustment can be carried out along with flow and load changes, and the scheduling precision and the resource utilization rate are improved. Experimental results show that the scheme improves the system throughput while guaranteeing the data privacy, and is suitable for scenes of security crowdsourcing, data centers, edge computing and the like.

The invention relates to the field of network communication, and discloses a fast message pushing method and device based on converged communication and a medium, and the method comprises the steps: collecting message content data through a multi-source heterogeneous interface, carrying out the normalization processing of the data, and generating standard message metadata; key feature vectors are extracted from the standard message metadata by adopting a deep semantic analysis model, and a message priority evaluation matrix is generated in combination with a user portrait database; based on a multi-channel intelligent routing algorithm, generating an optimal transmission path decision tree according to the priority evaluation matrix and the real-time network state data; and distributing the message to the target communication channel according to the decision tree path through a dynamic load balancing engine, and executing multi-protocol adaptive transmission. According to the method, a whole-process message pushing method from matrix evaluation to intelligent routing and then to multi-protocol adaptive transmission is constructed, a high-reliability and low-delay communication solution is provided for a scene needing rapid message pushing, and the method has remarkable technical breakthrough and commercial application value.

The invention relates to the technical field of server balancing, in particular to a server dynamic load balancing and resource management method which specifically comprises the following steps: acquiring CPU utilization rates of a plurality of servers in real time, grouping the plurality of servers according to the acquired CPU utilization rates of the plurality of servers, dividing the plurality of servers into priority queues and idle queues, the method comprises the following steps: acquiring a priority queue, arranging a plurality of servers in the priority queue from low to high along the CPU utilization rate, acquiring a real-time service request, executing the acquired service request according to the arrangement sequence of the priority queue, analyzing load fluctuation in a future time period, and performing elastic capacity expansion and contraction operation on the servers according to the acquired load fluctuation. According to the method, the CPU utilization rate of the server is monitored in real time, the server grouping and task allocation strategy is dynamically adjusted, and the future load fluctuation trend is analyzed in combination with historical data, so that intelligent management of server resources is realized, and the problem that part of servers are overloaded and part of servers are idle is effectively avoided.

The invention, which relates to the technical field of the new energy vehicle power supply system, discloses a dynamic load balancing power supply system for a new energy vehicle charging pile cluster, comprising a data acquisition module, a load prediction and decision module, a dynamic power distribution execution module and a safety protection and communication module. The data acquisition module is used for monitoring three-phase electrical parameters, temperature and electric energy quality data of the charging pile cluster in real time; the load prediction and decision module is connected with the data acquisition module and is used for predicting a load trend based on historical and real-time data and generating a dynamic regulation and control strategy; the dynamic power distribution execution module is connected with the load prediction and decision module, accurate prediction and rapid dynamic regulation and control of the charging load can be achieved, the problems of three-phase imbalance, transformer loss, harmonic pollution and the like of a power distribution system caused by randomness and volatility of charging behaviors are effectively solved, and the intelligent graded safety protection capability is achieved.

The invention discloses an on-chip network system based on cache consistency, belongs to the technical field of computer network communication, and aims to solve the technical problems of global consistency flow overload, on-chip network scheduling delay, directory node load imbalance and too high core resource occupation of a traditional router architecture. Comprising a multi-core cluster, distributed directory nodes, a multi-channel network-on-chip and a hardware acceleration module, cores are connected through a bus, and local cache consistency is achieved based on an MSI protocol; a directory node divides a physical address space into a plurality of fragments based on an address hash fragmentation mechanism, each directory node maintains a sparse directory table, and when the length of a request queue of any directory node exceeds a set threshold value, a dynamic load balancing mechanism is triggered; the multi-channel network-on-chip provides a control channel, a data channel and a configuration channel, and each channel performs message scheduling based on a dynamic priority mechanism; and the hardware acceleration module is used for processing three types of consistent transactions.

The invention discloses a distributed data acquisition system, and relates to the technical field of data acquisition, the system can improve the data acquisition efficiency through a dynamic load balancing distributed data acquisition method, data acquisition tasks are distributed to a plurality of nodes, the parallel processing capability is utilized, and the data acquisition efficiency is improved. And the time required by the whole data acquisition can be obviously reduced. Dynamic load balancing can ensure relative balancing of loads among the nodes, and the condition that some nodes are overloaded and other nodes are idle is avoided, so that the acquisition efficiency of the whole system is maximized. In a distributed system, a single point of fault may cause paralysis of the whole system. By means of dynamic load balancing, it can be ensured that when a certain node breaks down, the load of the node can be rapidly transferred to other healthy nodes, and therefore the continuity and stability of the system are kept.

The invention discloses an intelligent dynamic load balancing method and system based on Clico-VPP. The method comprises the following steps: acquiring a load state index of a back-end service pod; according to the load state index and a service capability coefficient corresponding to the back-end service pod, calculating a dynamic traffic allocation weight of the back-end service pod through a weighted calculation model; based on a dynamic threshold synchronization strategy, synchronizing the state of a first hash ring located in a control plane to a second hash ring located in a forwarding plane; and based on the second hash ring, determining a target pod for the input data stream through consistency lookup. A double-loop decoupling architecture is provided, hash rings of a control plane and a forwarding plane are separated, a synchronization strategy is determined through a dynamic threshold value, decision intelligence and forwarding stability are both considered, and jitter is avoided; meanwhile, a dynamic weight-driven consistent Hash algorithm is adopted, a real-time load index and Pod service capability are fused to calculate a dynamic weight, and the dynamic weight is mapped into the number of virtual nodes, so that intelligent scheduling based on capability and maximum maintenance of connection during capacity expansion and contraction are realized.

The invention discloses an inter-host process migration method and system, computer equipment and a medium. The inter-host process migration method comprises the steps of obtaining a memory snapshot, file descriptor information, network state information and register state information of a target process in a source host; determining memory mapping information based on the memory snapshot; sending the memory mapping information, the file descriptor information, the network state information and the register state information to a target host; and performing target process reconstruction in the target host by utilizing the memory mapping information, the file descriptor information, the network state information and the register state information. According to the scheme provided by the invention, the memory copying and recovery of the running process can be realized, the resource continuity and the migration reliability are ensured by combining the file descriptor information, the network state information and the register state information, and the method is suitable for dynamic load balancing and fault recovery scenes in cloud computing and distributed systems.

The invention relates to the technical field of computer hardware resource management, in particular to a dynamic load balancing optimization system for a computer mainboard, which comprises a data acquisition module, a feature decoupling module, a load evaluation module, a topology construction module and a strategy generation module. The system collects real-time load data of hardware components of a mainboard, and calculates intensive, data throughput and input and output blocking load features through feature decoupling separation. And generating a load pressure index and a migration potential coefficient of each component based on the decoupling characteristics, and dynamically constructing a mainboard resource topological graph. The system simulates the influence of different load migration schemes on delay and energy consumption on a topological graph, and generates a collaborative optimization instruction set integrating task scheduling, frequency adjustment and power distribution strategies. According to the method, fine identification of load characteristics and global collaborative scheduling of system resources are realized, and the resource utilization efficiency and the energy efficiency ratio are improved.

Methods, apparatus, systems, and articles of manufacture are disclosed to improve webservers using dynamic load balancers. An example method includes identifying a first and second data object type associated with media and with first and second data objects of the media. The example method also includes enqueuing first and second event data associated with the first and second data object in a first and second queue in first circuitry in a die of programmable circuitry. The example method further includes dequeuing the first and second event data into a third and fourth queue associated with a first and second core of the programmable circuitry, the first circuitry separate from the first core and the second core. The example method additionally includes causing the first and second core to execute a first and second computing operation based on the first and second event data in the third and fourth queues.

The invention discloses a dynamic load balancing method based on node and task classification matching, which relates to the field of distributed computing and comprises the following steps of: dividing nodes in a distributed computing environment into a computing type, a communication type, a storage type and a universal type based on quantitative indexes, the tasks are divided into a calculation-intensive type, a communication-intensive type, a storage-intensive type and a universal type based on a demand proportion; calculating a basic adaptation degree of the task and the node, and adjusting the adaptation degree to obtain a final adaptation degree; determining the total weight of the nodes, and performing initial task allocation through a multi-target weighted scoring strategy in combination with a task load quantification result, execution time estimation and a final adaptation degree; and performing node load state monitoring based on the load balancing factor, and triggering task migration to realize dynamic load balancing. According to the method, the system resource utilization rate, the task execution efficiency and the service quality are remarkably improved through classification matching and dynamic load balancing of the nodes and the tasks.

The invention discloses a data center load balancing system and method based on an SDN (Software Defined Network). The system comprises a switch monitoring module, a load balancing module and a flow table issuing module. The method comprises the following steps: periodically obtaining network global state information through an in-band network telemetry INT technology; calculating a global average priority based on the INT data to distinguish high and low priority data streams; dynamically calculating an optimal path in K shortest paths for avoiding congestion nodes for the high-priority flow, and selecting a path for the low-priority flow by adopting an improved ECMP algorithm based on link utilization rate weighting; and finally compiling the path strategy into a flow table item and issuing the flow table item to the switch. Through a priority-aware dynamic load balancing mechanism, the Hash collision and static defects of the traditional ECMP are overcome, the transmission delay of the high-priority data flow is effectively reduced, the bandwidth utilization rate of the low-priority data flow and the whole network flow transmission quality are improved, and the method is suitable for a large-scale data center network.

The invention relates to the crossing field of computer simulation and aerospace propulsion technology, and discloses a numerical simulation method for solid propellant grain cutting treatment, which comprises the following steps: establishing a three-dimensional model of a grain and a cutting tool, and defining a heat-force-damage coupling constitutive relation and boundary conditions; performing multi-physical field monitoring based on the weighted combination of the equivalent plastic strain, the temperature and the damage gradient at each time step; dynamically executing self-adaptive encryption and coarsening of the grid; in combination with dynamic load balancing in parallel computing, the resource utilization rate is increased; and material separation is simulated through a damage fracture criterion, and finally high-precision cutting force, a temperature field, a stress-strain field and a cutting morphology are output. According to the method, the simulation precision and efficiency are improved, and reliable support is provided for process optimization and safety evaluation.