12results about How to "Improve task scheduling efficiency" patented technology

The invention discloses an intelligent task scheduling and load balancing regulation and control method for a data center, and the method comprises the steps: 1, the construction of a learning model, 2, the construction of a computing power demand model, 3, the preliminary construction of a balancing regulation and control system, 4, the improvement of the balancing regulation and control system, and 5, the introduction and application of the system. The system comprises a data acquisition system, a data processing system and a distribution scheduling system. According to the method, adaptive learning adjustment and optimization are carried out on the task scheduling strategy of the data center by constructing the adaptive updating deep reinforcement learning model, the task scheduling efficiency is gradually improved, tasks are intelligently allocated on the basis of considering renewable energy source supply and electricity price fluctuation, stable resource utilization is ensured, and the task scheduling efficiency is improved. And meanwhile, the execution time which can be delayed by the tasks is fully considered, and the emergency tasks are preferentially allocated, so that the reduction of the overall energy consumption of the data center and the optimization and improvement of the performance are realized, and a better intelligent regulation and control direction is provided for the balance of resource utilization and energy efficiency management of the data center.

The invention discloses a task scheduling method and device, equipment, a medium and a product. The method comprises the steps that in the process that a heterogeneous parallel system obtains tasks, if it is detected that a new task arrives, whether task scheduling conditions are met or not is determined according to the number of arrived tasks and the initial time when the first task arrives; if yes, determining a candidate scheduling sequence corresponding to the to-be-allocated task, and determining candidate completion time required for completing execution of the to-be-allocated task under each candidate task scheduling sequence according to a preset task scheduling information table; and determining a target task scheduling sequence according to the candidate completion time corresponding to each candidate task scheduling sequence, so as to distribute the to-be-distributed task to a corresponding target execution server to complete task scheduling. According to the technical scheme, the batch tasks can be distributed and scheduled in a unified mode, the task scheduling efficiency of the heterogeneous parallel system is effectively improved, and computer resources are saved.

The invention discloses a heterogeneous multi-core task scheduling method based on a whale optimization algorithm. The heterogeneous multi-core task scheduling method comprises the following steps: step 1, task information acquisition and model establishment; firstly, to-be-scheduled task information needs to be obtained, and the task information comprises task types, calculation amount, memory requirements and communication overhead; then, according to the architecture characteristics of the heterogeneous multi-core processor, establishing a task scheduling system model; 2, the number of white whale individuals is initialized and corresponds to a task scheduling scheme; 3, solving an individual with the highest fitness based on a white whale optimization algorithm, and taking the individual as an optimal solution; and 4, determining a final task scheduling scheme according to the optimal solution, wherein the final task scheduling scheme comprises a processor to which each task is allocated and an execution sequence. 5, the scheduling scheme is applied to the heterogeneous multi-core system, and task scheduling is executed. According to the method, the task scheduling efficiency is remarkably improved, and the overall task completion time is shortened.

The invention discloses a task scheduling method and device, a controller, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-executed task; determining a thread matched with the task priority from a plurality of threads of a current system according to the task priority of the to-be-executed task; and adding the to-be-executed task into a task queue corresponding to the thread, and processing each task based on the execution time information of each task in the task queue. The embodiment of the invention not only can reduce the complexity of embedded system software development, but also can support synchronous operation of a plurality of timed tasks.

The invention relates to the technical field of cloud computing, and discloses a computing task allocation method and system based on a cloud platform, and the method comprises the steps: carrying out the optimization through taking the minimization of the total delay of task computing as an optimization target, and obtaining a computing node with the minimum total delay of a computing task; and the optimal path reaching the optimal calculation node is determined in the directed graph with the edge weight, so that the total delay of the calculation task is further reduced, rapid dynamic response to calculation task scheduling is realized, the task scheduling efficiency of the calculation task is improved, and the stability and reliability of system operation are ensured.

The embodiment of the invention discloses a task scheduling method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a task share value of each task queue scheduled by each historical scheduling round in a plurality of recent historical scheduling rounds, and obtaining a plurality of reference share values of each task queue, each task queue is used for caching one task type in multiple task types; based on the full-load share value of each task queue and the corresponding reference share value, determining the scheduling share value of each task queue in the round; and based on the corresponding scheduling share value of the current round, performing scheduling of the current round on the tasks of the task queues. According to the embodiment of the invention, the utilization rate of server resources and the task scheduling efficiency can be improved.

The invention discloses a multi-dimensional resource task scheduling method based on reinforcement learning in a computing power network environment, and belongs to the field of multi-dimensional resource task scheduling. The method comprises the steps that a user in a cloud computing environment submits a request through a cloud computing platform, the cloud computing platform processes the user request, decomposes a task, establishes a reinforcement learning model of multi-dimensional resources, trains the reinforcement learning model of the multi-dimensional resources, and achieves multi-dimensional resource task scheduling based on reinforcement learning. Through the reinforcement learning method, according to the real-time states of the tasks and the resources, task allocation and resource scheduling of the multi-dimensional resources are dynamically performed, the response speed of the cloud computing platform is improved, the energy consumption of the cloud computing platform is reduced, and the resource utilization rate and the task completion time of the cloud computing platform are improved.

This application provides a task scheduling method and system, electronic device, and storage medium, belonging to the field of task scheduling technology. The method includes: acquiring historical site data for all sites within a scheduling area and historical video recording data for all cameras within each site; determining the pre-allocated task volume for each site under different sampling types based on current scheduling resources, historical site task volume, and historical site event volume; determining the first correlation degree between the corresponding camera and the occurring event based on the historical video task volume and historical event volume; selecting multiple target camera devices from all camera devices within the site based on the first correlation degree of each camera device; and generating target scheduling data for each target camera device in the site based on historical time period data, the pre-allocated task volume corresponding to different sampling types, the multiple target camera devices, and the tasks corresponding to the site. This application can improve the efficiency of task scheduling.

This application relates to a partitioning method, apparatus, computer device, storage medium, and computer program product based on edge number optimization. The method includes: acquiring a target directed acyclic graph (DAG) for processing a target task; the target DAG includes multiple partitions; calculating the edge gain values ​​of the corresponding boundary task nodes according to the node order of the boundary task nodes in the target DAG; the boundary task nodes are task nodes in partitions different from the first partition that have dependencies on each other, and the first partition is the partition of the boundary task nodes in the target DAG; updating the partition position of the corresponding boundary task node in the target DAG based on the edge gain values ​​of the corresponding boundary task node. This method can improve task scheduling efficiency.

The application discloses a multi-task scheduling method of a heterogeneous processor and belongs to the computer field. In the case that the storage resource of an end side is insufficient, the user cannot use a single model to parallel infer different frame data, and the existing resources are reused as much as possible to improve the inference efficiency in the scene that the camera data / frame data needs to be processed in parallel. Meanwhile, different priorities are set for different inference tasks and different inference tasks of the same model, multiple models exist in cascade, and the multiple model cascades return in the middle, and the multi-task scheduling method is provided, which can effectively improve the efficiency of the end side inference task and significantly reduce the waste of the computing resource.

The embodiment of the present invention provides a method for scheduling video interpolation tasks based on deep reinforcement learning, which belongs to the field of data processing technology, and specifically includes: step 1, decoding video data to obtain different video frame groups and dividing interpolation algorithm types to obtain video frame group feature information vectors; step 2, obtaining computing power equipment performance index vectors; step 3, based on the video frame group feature information vectors and computing power equipment performance index vectors, using reinforcement learning to allocate computing power equipment to different video frame groups; step 4, obtaining interpolation results from each computing power device, inserting the interpolation results between video frames, obtaining a complete interpolation video group, encoding it, and obtaining interpolation video data. Through the scheme of the present invention, the efficiency, accuracy and adaptability of task scheduling are improved.

This disclosure provides a task scheduling method, a task scheduler, a processing device, an electronic device, and a medium. The method, applied to a task scheduler in a processing device, includes: comparing the priorities of second tasks in a first task queue and a second task queue to determine a priority comparison result; determining a load timeout result for a target task queue based on the priority comparison result, wherein the target task queue is a task queue with a higher priority or equal priority among the first and second task queues; determining a task boundary judgment result for the first task queue based on shared resource usage information of the first task queue; determining a scheduling result for a first task currently awaiting execution in the first task queue based on the priority comparison result, the load timeout result, and the task boundary judgment result; and, if the scheduling result allows for delivery, delivering the first task to a backend module. Embodiments of this disclosure can improve the efficiency of task scheduling.