187 results about "Low resource" patented technology

The invention discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention provides a resource scheduling control method and system for a big data server, and the method comprises the steps: constructing a multi-dimensional resource portrait module, collecting the CPU, memory, network, storage I / O load and task queue length of each node in real time, and predicting a resource demand trend through a time sequence algorithm; extracting characteristics such as calculation intensity, data dependence, memory requirements, network transmission quantity and the like; adjusting the weight coefficients of the resource utilization rate, the task completion time and the energy consumption efficiency according to the system load and the historical effect; establishing a bipartite graph model by taking a resource trend as a node feature and a task vector as an edge feature, and calculating a matching score through graph convolution and a multi-objective optimization function; the scheduling scheme is synchronized by adopting a consistency algorithm; automatic rollback and reallocation are carried out when resources are detected to be insufficient; and optimizing a weight coefficient and a network parameter through reinforcement learning. Through the method, the system resource utilization rate can be improved, the task execution efficiency is improved, the overall scheduling effect stability is improved, and the system fault recovery time is shortened.

The embodiment of the invention discloses a distributed computing power resource scheduling method and system, a storage medium and a program product. The method comprises the following steps: decomposing a composite computing task into a plurality of sub-tasks and constructing a dependency graph; obtaining demand resource information of each subtask and constructing a demand resource matrix according to the demand resource information; obtaining performance data of the global computing power node and constructing a global dynamic resource portrait according to the performance data; dividing global computing power nodes into different areas based on the global dynamic resource portrait; based on the demand resource matrix and the performance data, determining an adaptive area of each sub-task from all the areas, and then determining adaptive computing power nodes of the sub-tasks from the adaptive areas of the sub-tasks; and sequentially executing each subtask based on the dependency graph and the adaptive computing power node of each subtask. According to the method, the problems of low cross-data center resource utilization rate, high burst task response delay and poor heterogeneous environment scheduling efficiency can be solved, the overall computing power resource utilization rate is improved, and the task average delay is reduced.

The invention discloses a cross-regional computing power resource collaborative allocation method based on computing power centers, and relates to the technical field of computing power resource scheduling and optimizing.The cross-regional computing power resource collaborative allocation method comprises the steps that real-time load data, historical task execution data and network delay data of all computing power centers are collected, and a regional load feature database is constructed; predicting the load demand of each computing power center in a future time window by using a long short-term memory network model to generate a load prediction value; calculating a resource gap coefficient and a resource margin coefficient of each region according to the load prediction value and the current resource capacity, identifying a resource insufficient region and a resource surplus region, and generating a resource collaborative matching matrix between the regions; and optimizing and solving the cross-regional task allocation scheme by adopting an improved genetic algorithm to generate an optimal resource allocation strategy, and allocating the to-be-processed task to a corresponding computing power center according to the optimal resource allocation strategy. According to the method, the cooperative utilization rate and the distribution efficiency of the computing power resources in the cross-regional scene are effectively improved.

The invention provides an internet of things equipment collaborative task scheduling optimization method and system oriented to edge computing, and relates to the technical field of the internet of things, and the method comprises the steps: collecting a task request from an internet of things terminal, and extracting task features; constructing a dynamic resource state table; generating a candidate node set; selecting a target computing node from the candidate node set, and deploying a task execution unit on the target computing node; generating a resource utilization rate index based on the real-time operation index of the node; and generating a task performance index based on task execution feedback, dynamically adjusting a task scheduling rule, or isolating an abnormal computing node from an abnormal task. Through the task scheduling method and device, the technical problems of low resource utilization rate and high response delay caused by lack of global collaboration of task scheduling in the prior art are solved, and efficient task allocation between edge devices is realized by extracting task features and establishing an optimization model, so that the overall scheduling efficiency and the system performance are improved.

The invention discloses an open architecture system for an underwater acoustic signal processor, and belongs to the technical field of underwater acoustic signal processors. The system comprises a hardware resource pool, a standardized algorithm container group, a visual processing link construction module, a containerized deployment and scheduling platform and a data processing and communication plane. According to the system, special hardware resources are pooled, an underwater sound processing algorithm is packaged into a standardized container, a user is allowed to arrange a processing flow in a graphical mode, an intelligent scheduling platform dynamically deploys the algorithm container to matched hardware for execution according to real-time resource conditions, and meanwhile, an efficient and reliable data communication channel is provided. According to the method, the problems that a traditional system is closed, poor in expansibility and low in resource utilization rate are solved, elastic sharing of hardware resources, agile deployment of an algorithm and flexible construction of a flow are achieved, and openness, flexibility and overall efficiency of an underwater acoustic signal processing system are remarkably improved.

The invention discloses a heterogeneous computing power resource dynamic scheduling system and method based on multi-objective optimization, and the system is deployed in a digital ecological platform complex based on multivariate consensus and embedded intelligent management. Comprising a platform access module, a data acquisition and perception module, a multi-target modeling and optimization module, a dynamic scheduling and execution module and a fault-tolerant mechanism module. The system registers as a computing power scheduling service node through an intelligent contract interface, obtains and verifies the compliance of a computing task, collects heterogeneous computing power resource node state data, and generates a scheduling decision by dynamically adjusting the weight of a target function; evaluating task suitability by using an AMCU suitability scoring model, and executing a scheduling AMCU strategy; according to the method, the technical problems of low resource scheduling efficiency and poor fault-tolerant capability in a heterogeneous computing power environment are solved, and the system throughput and the resource utilization rate are improved.

The invention belongs to the field of computer resource management, particularly relates to a computer resource dynamic allocation management method based on cloud computing, and aims to solve the problems of locality of a decision view angle and hysteresis of a time dimension in related technologies. The method comprises the following steps: acquiring the resource utilization rate of each virtual machine in a cloud environment, wherein the resource utilization rate comprises a historical resource utilization rate and a real-time resource utilization rate; according to the resource utilization rate, predicting a resource demand of each virtual machine in a future preset time period; determining a resource competition conflict according to the predicted resource demand, and constructing a resource competition graph model; mapping the resource competition graph model into a non-cooperative game model; and determining a scheduling scheme in the cloud environment based on the non-cooperative game model, and executing a resource allocation operation according to the scheduling scheme. According to the method, potential conflicts can be identified and scheduled before resource competition actually occurs, and performance reduction caused by insufficient resources is reduced.

The invention relates to the technical field of meteorological and ocean numerical forecasting, and aims to solve the problems of strong manual dependence, poor timeliness, high error risk and low resource utilization rate in coupling operation of a WRF atmosphere model and an ROMS ocean model. The method comprises the following steps of: storing an initial file into a date catalogue which is dynamically generated according to a YYYMMDD format after the initial file is subjected to integrity verification; the timed task checks nc.lock / nc.tone files every X minutes, a lock starting model is created when no lock exists and an initial file exists, and an ROMS needs to confirm nc.tone of a WRF result directory first; automatic exception processing is carried out when the model is overtime, and after completion of the automatic exception processing, a Done is created, a lock is deleted, resources are released, and computational nodes are dynamically allocated. According to the method, automatic scheduling is achieved through double-file marking, manual operation is shortened to be within one minute, model connection delay is shortened to be within five minutes, the failure rate is reduced, the resource utilization rate is improved, and a reliable scheme is provided for model business operation.

The invention discloses an idle computing power resource dynamic scheduling method based on node allocation, and relates to the technical field of node allocation. Sequentially executing to-be-scheduled calculation tasks to perform lightweight simulation execution to generate task feature vectors, collecting idle node hardware architecture feature data to generate node hardware feature tags, calculating a matching degree based on a bidirectional adaptability evaluation matrix, allocating main execution nodes, and performing task scheduling; the redundancy is determined by combining the task key level and the overall reputation score of the node pool, the standby redundant nodes are allocated for parallel execution, and the closed-loop scheduling process of the consensus verification window period and the arbitration mechanism is synchronously set, so that compared with the prior art, the accuracy of idle-time computing power scheduling and the credibility of an execution result can be improved, and the scheduling efficiency is improved. Therefore, the problems of low resource utilization rate and high error rate of calculation results caused by disjunction of task and node adaptation and lack of effective verification in the execution process in the existing idle computing power scheduling can be solved.

The invention relates to the technical field of resource scheduling, in particular to a server resource optimization scheduling method and system. The method comprises the steps that node index data, a historical load data set and environment parameters of a current server within a period of time are collected, and the node index data comprise the node-level CPU utilization rate, the memory occupancy rate, the disk I / O throughput and the network bandwidth utilization rate; based on the node index data, the historical load data set and the environmental parameters, performing parallel hybrid prediction by using a time sequence prediction model and a trend decomposition model to obtain predicted load data; inputting the predicted load data into a preset optimization model for optimization processing, and determining adjustment strategy parameters; and scheduling the scheduling resources of the current server by using the adjustment strategy parameters to determine an optimal scheduling scheme of the server resources. The invention aims to solve the problems that the dynamic response delay of the existing scheduling method is high, the service degradation is easily caused, and the resource allocation efficiency is low.

The invention discloses a multi-dimensional parallel artificial intelligence processor for large model reasoning, relates to the technical field of artificial intelligence processors, and solves the problem that fixed and rigid hardware parallel configuration cannot adapt to a dynamically changing reasoning load. In order to solve the problems of low resource utilization rate, aggravated communication conflicts and unstable overall performance caused by the fact that a current task is not required, the number and the type of required virtual modules are accurately calculated according to the batch size and the sequence length which are changed in real time, and physical calculation units are dynamically combined according to the number and the type of the required virtual modules, so that processor resources can elastically match the actual requirements of the current task; the contradiction that small tasks occupy large resources or large task resources are insufficient under fixed configuration is fundamentally overcome, so that the average utilization rate of the computing unit is improved, and invalid power consumption is reduced.

The invention provides an intelligent resource dynamic sharing and task scheduling method based on a cloud desktop system. According to the method, a resource monitoring module is deployed on a local cloud desktop to monitor the utilization rate of key resources in real time, resources are requested from a cloud desktop system when the resources are insufficient, a cloud desktop resource management module selects a proper cloud desktop to provide the resources, and tasks are reasonably allocated by using a resource allocation algorithm. Meanwhile, efficient operation of the compute-intensive application in the cloud desktop system is realized through task scheduling and monitoring, data synchronization and a feedback adjustment mechanism. According to the method, the resource utilization rate is improved, the task execution time is shortened, the system stability is enhanced, the cost is reduced, and the problems that an existing cloud desktop system is unreasonable in resource allocation and low in task execution efficiency are effectively solved.

The invention provides a task scheduling method and system and related equipment, and can be applied to the technical field of computers. The method comprises the steps that after a target task comprising a plurality of sub-tasks is obtained, computing resources needed for executing each sub-task are obtained, and target computing resources needed for executing the target task are determined according to the computing resources corresponding to each sub-task. Then, available computing resources of each computing cluster in the multiple computing clusters are obtained, a target computing cluster meeting the target computing resources is allocated to the target task according to the available computing resources of each computing cluster, and the target task is sent to the target computing cluster to be executed. Therefore, according to the task scheduling method, the target task can be allocated to the computing cluster with sufficient resources or relatively low resource utilization rate for execution, so that reasonable allocation and utilization of the cluster resources are realized, and the execution efficiency of the target task is improved.

Non-transitory machine-readable storage medium (300; 404) which contains instructions which, when executed, cause a system (400) to execute: Monitoring (502) of a network (109) comprising a collection of connected virtual network functions, VNFs (108; 108-1; 108-2; 108-3), wherein the monitoring (502) comprises receiving control information (116) from a first VNF ​​(108; 108-1; 108-2; 108-3) of the VNFs (108; 108-1; 108-2; 108-3) and receiving control information (116) from a second VNF (108; 108-1; 108-2; 108-3) of the VNFs (108; 108-1; 108-2; 108-3), and wherein the control information (116) specifies at least one action to be taken to address a problem that is the first VNF ​​(108; 108-1; 108-2; 108-3) and / or the second VNF (108; 108-1; 108-2; 108-3); Determine that the control information (116) from the first VNF ​​(108; 108-1; 108-2; 108-3) indicates that the first VNF ​​(108; 108-1; 108-2; 108-3) is overloaded and / or allocated resources are insufficient; Determine that the control information (116) from the second VNF (108; 108-1; 108-2; 108-3) indicates that the second VNF (108; 108-1; 108-2; 108-3) is over-provisioned with resources and / or allocated resources exceed a load of the second VNF (108; 108-1; 108-2; 108-3); and Modifying (504) an allocation of resources to the first VNF ​​(108; 108-1; 108-2; 108-3) and to the second VNF (108; 108-1; 108-2; 108-3) in response to monitoring (502), where modifying (504) the allocation involves allocating resources from the second VNF (108; 108-1; 108-2; 108-3) to the first VNF ​​(108; 108-1; 108-2; 108-3).

The embodiment of the application provides a kind of determination method of abnormal request, this method includes: through the tracker deployed in node, corresponding entry is allocated for each request to node and the entry identification of generating entry identification;According to the generation order of entry identification, generate entry identification chain, wherein, entry identification chain includes entry identification and the validity state of entry identification;Through the polling detection of request in timer in tracker, determine timeout request, and the request corresponding to the first entry identification of chain is determined as abnormal request;Wherein, the first entry identification of chain is the earliest generation and the validity state is valid in entry identification chain.The technical problem that related art cannot accurately detect and locate abnormal request under the condition of low resource consumption is solved by the embodiment of the application.

The invention discloses a distributed data processing method and system for an intelligent factory, and relates to the technical field of communication data processing. Determining target equipment according to the original data; based on the centrality index of the target equipment in the factory equipment topological graph, calculating the centrality index to obtain a topological criticality score; analyzing the original data based on a preset rule base to obtain an event severity score; multiplying the topology criticality score by the event severity score to obtain a final priority; inserting the original data into a priority scheduling queue according to the final priority; the storage nodes with the resource loads lower than a preset health threshold value are summarized into a current available node pool; and taking out the target data with the highest final priority from the head of the priority scheduling queue, selecting a storage node with the lowest resource load from the current available node pool as a target storage node, and preferentially distributing the target data to the target storage node for processing. By implementing the technical scheme, priority processing of high-value data is ensured.

The invention discloses an agent application-oriented resource sharing method, and relates to the field of cloud computing. The scheme comprises offline and online two-stage processing: in the offline stage, constructing an agent dependency conflict graph, dividing a core and other agent sets and grouping, and determining candidate execution nodes through a convergence hash algorithm; in the online stage, a global scheduler selects a target node according to grouping information and node real-time loads, a local scheduler schedules a calculation sandbox to execute local logic, a communication sandbox calls a large language model, and a capacity sandbox is dynamically expanded and shrunk based on a preset strategy. The system correspondingly comprises an offline processing module, a global scheduler, a local scheduler and two types of sandboxes. Through calculation and communication decoupling, off-line grouping, double-layer scheduling and security isolation mechanisms, the problems of low resource utilization rate, high operation cost, insufficient concurrent bearing capacity and the like in the prior art are solved, and the resource reuse rate, the system stability and the throughput capacity are remarkably improved.

Example embodiments of the present disclosure relate to a terminal device, a network device, methods, apparatuses, a computer readable storage medium, and a computer program product for data transmission of a haptic / sensing traffic in a multiple-modality XR service. In the solution, the terminal device is configured with a first resource. The terminal device transmits a first part of data using the first resource and an indication along with the first part of data, if the first resource is not enough for a transmission of the data. The indication is used for indicating that a second resource is required, accordingly the second resource may be allocated to the terminal device in time. As such, a low latency and high service reliability requirement of data packets of haptic / sensing traffic can be supported.

The invention discloses a historical load and real-time state-based resource available amount prediction method and system, and relates to the technical field of big data analysis, and the method comprises the steps: collecting data to form a CPU data set and a GPU data set; removing abnormal values and carrying out feature coding to form a structured feature matrix; extracting a CPU time sequence rule, and introducing an attention mechanism to dynamically adjust and predict a CPU available interval; capturing GPU long-period stage features, and predicting a GPU available interval in combination with a historical stage computing power demand rule; and integrating the available intervals of the two into a global resource matrix, and matching new task resource requirements to judge whether migration is needed or not. The system comprises a data acquisition module, a feature processing module, an available prediction module and a task judgment module which work cooperatively. According to the method, the historical load and the real-time state are fused, the prediction accuracy of the available resource amount is improved, resource scheduling is optimized, failure of tasks due to insufficient resources is avoided, and the computing power resource utilization rate is increased.

This application discloses an electronic device, a management controller, a firmware upgrade system, and a firmware upgrade method, relating to the field of server hardware technology. The electronic device includes a management controller and a hardware partitioning system. The management controller includes a first processor core and a second processor core. A first operating system runs on the first processor core, and a second operating system runs on the second processor core. The response time of the first operating system is higher than that of the second operating system. The hardware partitioning system includes multiple hardware partitions. The first processor core is used to receive a firmware file to be upgraded and to pass the firmware file to be upgraded to the second processor core. The second processor core is used to write the firmware file to be upgraded to at least one of the multiple hardware partitions for firmware upgrade. This solves the technical problems of resource redundancy and low resource utilization in related electronic devices.

The invention relates to the field of information technology processing, in particular to a data optimization storage method and device based on storage medium merging, and the method comprises the following steps: dividing time series data in a memory into time buckets with different time granularities according to a timestamp and a key identifier; different merging trigger conditions and merging granularity strategies are configured for different time buckets; an optimized data storage structure is adopted; sorting the index blocks according to a combination granularity strategy, and loading the data blocks pointed by the index blocks in sequence; performing sorting and merging operation on the data blocks in the memory, and immediately writing the data blocks into a target storage medium; and iteratively executing until the data of all the target time buckets are stored. The equipment is used for executing the steps described by the method. According to the method, the problems of overhigh system memory occupation, low resource utilization rate and poor system expansibility are solved, the system performance and stability are improved, and the large data volume processing capacity of the system is enhanced.

The present disclosure provides a hardware resource allocation method, device and equipment of an electronic device, which preliminarily identifies hardware power consumption mutation by sampling the hardware power consumption data of the electronic device in real time and determining the fluctuation index corresponding to the current sampling data. Further, the exponential weighted value of the current sampling data is determined, and when the current exponential weighted value and the exponential weighted value when the last fluctuation index meets the set condition, the mutation is determined as an effective mutation. Furthermore, when the fluctuation index and the exponential weighted value meet the condition at the same time, the future hardware power consumption data is further predicted, and the hardware resource demand information of the electronic device in the next target period is determined in combination with the fluctuation index. The fluctuation of the hardware power consumption data can cover the user's fine-grained operation, and by predicting the future hardware resource demand information, the resources can be prepared in advance, avoiding the situation of insufficient or lagging resources during scene switching, and ensuring smooth user experience.

The invention provides a power distribution network resource scheduling method and system based on computing power network integration, and belongs to the technical field of data processing and resource scheduling, and the method comprises the steps: obtaining a plurality of communication signals and a plurality of corresponding service requests, and packaging the communication signals and the service requests into a plurality of standardized data; corresponding response tasks are generated according to the service requests, and task resource requirements of the response tasks are determined; performing resource scheduling on each response task according to each task resource demand and a preset resource scheduling model, and determining resource configuration of each response task; according to each resource configuration, scheduling and allocating a plurality of virtual resources to each response task from a preset resource pool; and according to each response task and a plurality of corresponding virtual resource units, carrying out data processing on each piece of standardized data, so that by implementing the method and the device, the problems of relatively low utilization rate of power distribution network resources and inflexible scheduling in the prior art can be solved.

The invention discloses a lightweight hash function construction method and device based on a chaos sponge structure, equipment, a storage medium and a program product, and relates to the technical field of encryption algorithms, and the method comprises an initialization stage, an absorption stage and an extrusion stage. Wherein in the initialization stage, input messages are subjected to standardized filling, and an initialized input sequence is obtained; in the absorption stage, the initialization input sequence and the external part of the internal state are subjected to XOR, and the internal state after XOR is obtained; diffusing the internal state after XOR through the dynamic replacement layer to obtain an absorbed internal state; and an extrusion stage: iteratively extracting Hash output based on the absorbed internal state. Due to the fact that the lightweight hash function which is optimized for low resources and provided with the dynamic replacement layer is designed, the hash function is instantiated through a sponge structure frame, the output length flexibility and the collision attack resistance are kept, and strict cryptographic analysis is supported.

The invention relates to a cooperative processing method for a reservoir project operation management task and a model manager thereof, and aims to realize optimization of a task allocation strategy by dynamically adjusting performance requirements and task excitation of asynchronous subtasks in combination with computing power of nodes and real-time asynchronous subtask progress so as to improve the task allocation efficiency. Therefore, the problems of large response delay and low resource utilization rate in a traditional centralized management mode are solved, and the method has the advantages of dynamically adjusting the task allocation strategy, optimizing the resource utilization rate, improving the task cooperation efficiency and shortening the overall task completion time.

This invention provides a method and apparatus for managing large transactions. When a scheduled task is executed, the method queries the running information of large transactions currently running in the system based on a script file generated in a specified directory. If the resources required for the SQL corresponding to the currently running large transaction are sufficient and the scheduled task has not ended, execution continues. If the required resources are insufficient, appropriate handling operations are performed according to the type of running information. In this embodiment of the invention, when a scheduled task is executed, the system transaction running status is automatically monitored. When a large transaction is identified, appropriate targeted handling is performed on different types of large transaction SQL information based on resource usage. Through fully automated monitoring and handling, not only can the risk of misoperation that may be caused by manual management and maintenance be avoided, greatly improving server stability, but also alarms can be responded to quickly, handling time can be shortened, and the purpose of reducing operation and maintenance difficulty and improving work efficiency can be achieved.

The method for fine-grained computing task division and scheduling of multi-intelligent application of the application divides the interrupted intelligent application into five types of subtasks, including data preparation, model preparation, hardware resource preparation, inference and result post-processing. According to the subtask attribute inheritance principle, the attribute five-tuple of each subtask is determined. All the interrupted intelligent application subtasks are input into the subtask scheduling sequence generation algorithm, the subtask application scheduling sequence is calculated, and the subtask scheduling sequence is added to the application scheduling sequence. Through the task division and subtask scheduling method, the problems of low resource utilization and high multi-task execution delay in the airborne environment are optimized. The multi-intelligent application collaborative operation process is optimized based on the intelligent application scheduling algorithm and the subtask scheduling algorithm.

The invention discloses a task frame extraction scheduling method and device and electronic equipment. The method comprises the steps that a target frame extraction task with a preset frame extraction interval is acquired, a frame extraction interval set of all scheduled frame extraction tasks in a server is acquired, and a task starting point corresponding to the target frame extraction task is a target task starting execution time point; determining the greatest common divisor corresponding to all the frame extraction intervals in the frame extraction interval set, and determining a unified execution period of the scheduled frame extraction task according to the greatest common divisor; all theoretical execution time points of the target frame extraction task in the unified execution period are determined, and the theoretical execution time points are determined according to a preset frame extraction interval and the target task initial execution time point; and verifying the resource availability of all the theoretical execution time points, and scheduling the target frame extraction task according to a verification result of the resource availability. The technical problems that a traditional fixed-frequency frame extraction strategy is difficult to adapt to complex and changeable task requirements, and the resource utilization rate is low are solved.