15 results about "Run queue" patented technology

The invention discloses a value maximization calculation unloading method and system based on dynamic priority scheduling and reinforcement learning. According to the method, an optimization framework with maximization of the'long-term average task value 'of the system as a core target is constructed, and the task value is defined by introducing an exponential value function, so that the problem of'value neglect' existing in a traditional'average delay or energy consumption optimization 'strategy is solved. According to the value-oriented optimization mechanism, the multi-agent system can autonomously learn a strategy which preferentially ensures high-value task execution when resources are limited in the training process. In addition, the invention further provides a dynamic priority function which can accurately measure the actual emergency level of the task at different moments, so that the defect that a static priority model is difficult to deal with the real-time change of the emergency state is overcome. Particularly, the dynamic priority method is not only used for task selection in a waiting queue, but also innovatively introduced into a resource dynamic allocation mechanism of a running queue.

This invention relates to the field of computer task management technology, and in particular to a task management method for Electron desktop applications based on IndexedDB storage. The method includes a task management module that listens for user-issued tasks; a task management trigger judgment method that modifies the queue type of a preset number of tasks to a running queue; a trigger method when the waiting task queue changes, wherein the waiting task queue enters the running task queue in a first-in-first-out manner; and a trigger method when the running task queue changes, executing the background interface or Electron's Node.js service corresponding to the task type of the unexecuted or paused tasks. This invention provides a complete desktop application task management solution that, compared to other storage methods, handles large-scale task processing with ease. IndexedDB's shared-source strategy ensures data security.

The quality of service guaranteed function-free computing system comprises an HTTP gateway, a scheduler and a work pool; the HTTP gateway serializes the incoming DAG request through a serializer, and sequentially adds the serialized function-free function in the DAG request to a waiting queue; the scheduler retrieves the function-free function from the waiting queue and the chain running queue to the work pool for processing according to a scheduling strategy. The quality of service guaranteed function-free system and method of the application process the incoming request through the producer-consumer mode by reforming the scheduler in the platform. In addition, a strategy framework containing rich strategies is added. Different kinds of strategies are set for different kinds of application requests, so that the queuing time is reduced to a certain extent, and the running performance is improved.

A method and apparatus for managing threads are provided. The method includes creating a first thread, the first thread being a kernel mode thread, the first thread having a first thread context; creating a second thread using the first thread, wherein the second thread is a user mode thread, and the second thread inherits the first thread context; controlling the first thread to enter an idle loop state after the second thread is enqueued to a run queue; and selecting and executing the second thread from the run queue using a scheduling thread.

The application discloses a frequency control method and device of a CPU core and electronic equipment, and relates to the technical field of terminals. The specific technical scheme is as follows: the electronic equipment can acquire an interactive thread list; the interactive thread list comprises thread information, which is used for indicating an interactive thread for processing a user-perceptible event; and a frequency upper limit value of a first CPU core and at least one second CPU core is adjusted according to a first load parameter and a first power consumption threshold value; wherein a running queue of the first CPU core comprises the interactive thread; the first load parameter is a load parameter of the first CPU core running the interactive thread; the at least one second CPU core is a CPU core other than the first CPU core; and the first power consumption threshold value is a total power consumption upper limit value of the first CPU core and the at least one second CPU core.

This invention relates to a user-mode thread scheduling method, apparatus, and storage medium based on behavior prediction. A short-term behavior predictor calculates prediction results using a hybrid model. A scheduling decision engine receives these prediction results, calculates a comprehensive benefit score based on the prediction results, and generates scheduling and migration decisions. A core affinity and migration controller executes migration decisions, controls the computation core to run CPU-intensive tasks, controls the I / O core to bind I / O interrupts and run I / O-intensive tasks, and controls the scheduling core to select user-mode thread tasks from a multi-level run queue and allocate them to the computation core or I / O core for execution. The scheduling and migration decisions involve resource allocation and priority adjustment. A lightweight behavior collector can collect the operational characteristics of user-mode threads with low overhead. While maintaining low overhead, a hybrid model is used to dynamically model thread behavior characteristics, predict short-term thread operational trends, and perform resource allocation and priority adjustment accordingly, thereby improving the overall system throughput.

The application provides an artificial intelligence-based task flow accurate scheduling method and device, electronic equipment and storage medium. The artificial intelligence-based task flow accurate scheduling method comprises the following steps: constructing a task link graph of each preset task flow, the preset task flow comprising a plurality of task nodes, the task link graph comprising a preset ID, a cycle type of the preset task flow and an upstream and downstream relationship of the task nodes; judging a dependency relationship between any task nodes in different task link graphs to draw a dependency graph, the dependency relationship comprising a dependent task node and a dependent cycle number; obtaining state information of each task node, the state information comprising a running queue and a running cycle number, the running queue comprising output results of the task nodes in each running cycle; calculating a scheduling priority of each task link graph based on the state information, the dependency graph, the cycle type and a preset priority; and scheduling the preset task flow corresponding to the task link graph according to the scheduling priority to obtain a scheduling result. The application can realize accurate scheduling of the task flow.

The invention provides a lift pump automatic operation method and system based on queue management, a medium and equipment. The lift pump automatic operation method comprises the following steps that state information of all lift pumps is obtained in real time; establishing a ready queue, a running queue and a start-stop queue of the lift pump based on the lift pump state information; the current operation number of lifting pumps and current water level information are obtained; based on the current water level information, the start-stop demand number of the lifting pumps is obtained; and adjusting the operation state of the lift pump based on a comparison result of the required start and stop number of the lift pump and the current operation number of the lift pump and queue information. According to the lift pump automatic operation method and system based on queue management, the medium and the equipment, a reasonable pump operation strategy is provided, and the problems that in the current production process, equipment use is not uniform, and water level fluctuation is large are solved.

This disclosure provides a task scheduling method, apparatus, and electronic device for a Completely Fair Scheduler (CFS), relating to the fields of artificial intelligence and cloud computing technologies, and particularly to operating system scheduling technology. The implementation scheme includes: determining the waiting time of ready tasks in the processor's run queue; updating the processor's latency information based on the waiting time of a ready task leaving the run queue when the ready task leaves; comparing the maximum waiting time of ready tasks in the run queue with a latency threshold, wherein the latency threshold is based on the processor's latency information; and scheduling the ready task corresponding to the maximum waiting time in response to the maximum waiting time exceeding the latency threshold. Using embodiments of this disclosure, the latency threshold for prioritizing task scheduling is dynamically determined by utilizing the processor's latency, thus achieving dynamic scheduling based on system operating conditions on top of CFS.

The present disclosure provides a video memory management method and device, equipment and medium, relates to the technical field of computers, and particularly to the field of artificial intelligence and intelligent chips. The implementation scheme is as follows: an operation queue of a target task is obtained; for each group of video memory management events, the group of video memory management events and all events between the group of video memory management events in the operation queue are encapsulated as an event function; for each event function in a plurality of event functions corresponding to a plurality of groups of video memory management events, first information of a video memory space corresponding to the event function is determined; and a plurality of sub-tasks are sequentially issued to a processing unit.

An electronic device according to an embodiment of the present disclosure may: select at least one process for memory recovery from among processes being executed in response to an out-of-memory event having occurred; add the at least one process to a first run queue managed by a first scheduler; and arrange processes to be terminated in the first run queue in the order of memory usage and sequentially terminate said processes.

The application discloses a processor task scheduling method and device and a storage medium. The application relates to the field of computer operating systems, and the method comprises the following steps: acquiring a centralized queue, wherein the centralized queue is used for storing a plurality of to-be-allocated tasks and priorities corresponding to the to-be-allocated tasks; when a running queue of a target processor is empty, selecting a to-be-allocated task stored in the centralized queue to obtain a first task, and reserving the first task for the target processor to add the first task to the running queue of the target processor; and determining a target task to be executed by the target processor according to the first task to control the target processor to execute the target task. The application solves the technical problem that the performance overhead of processor task scheduling is high.

The invention provides a task scheduling method and device for a completely fair scheduler and electronic equipment, and relates to the technical field of artificial intelligence and cloud computing, in particular to an operating system scheduling technology. According to the implementation scheme, the waiting duration of a ready task in a running queue of a processor in the running queue is determined; when the ready task leaves the running queue, delay information of the processor is updated according to waiting duration of the task; comparing the maximum waiting time length of the ready task in the running queue with a delay threshold value, wherein the delay threshold value is based on delay information of the processor; and in response to the fact that the maximum waiting duration exceeds the delay threshold value, scheduling and executing a ready task corresponding to the maximum waiting duration. By utilizing the embodiment of the invention, the delay threshold value for preferentially scheduling the task is dynamically determined by utilizing the delay condition of the processor, so that dynamic scheduling based on the running condition of the system is realized on the basis of CFS.

The present disclosure provides a task scheduling method and device, equipment and a storage medium, relates to the technical field of computers, in particular to the technical fields of cloud computing, data processing, task scheduling and the like. The specific implementation scheme is as follows: obtaining at least two tasks in a running queue; scheduling the at least two tasks in a central processing unit of cloud computing resources based on the task types of the tasks; wherein the task types include a virtual machine thread type, a system thread type and a best effort thread type; wherein the priority of the virtual machine thread type is higher than that of the system thread type, and the priority of the system thread type is higher than that of the best effort thread type. The present disclosure can improve the execution stability of the virtual machine thread type task.

This invention discloses a value maximization computational offloading method and system based on dynamic priority scheduling and reinforcement learning. The invention constructs an optimization framework with the core objective of maximizing the system's "long-term average task value." By introducing an exponential value function to define task value, it addresses the "value neglect" problem inherent in traditional "average latency or energy consumption optimization" strategies. This value-oriented optimization mechanism enables multi-agent systems to autonomously learn strategies to prioritize high-value tasks during training when resources are limited. Furthermore, this invention proposes a dynamic priority function that accurately measures the actual urgency level of tasks at different times, thus overcoming the shortcomings of "static priority" models in handling real-time changes in urgency. Notably, this dynamic priority method is not only used for task selection in the waiting queue but is also innovatively introduced into the dynamic resource allocation mechanism of the running queue.