9 results about "Cyclic scheduling" patented technology

The invention discloses a cyclic resource scheduling method, system and device based on Kubernetes and a medium, and the method specifically comprises the steps: collecting resource use data of Pod and nodes in a Kubernetes cluster in real time, and predicting a load trend; dividing the resources into a hot resource pool, a cold resource pool and an idle resource pool based on the prediction results of the resource use data and the load trend; according to the electricity price and carbon emission data of each region, dynamically adjusting task distribution, and preferentially scheduling the task of the thermal resource pool to a region with a high renewable energy source proportion; for the cold resource pool and the idle resource pool, executing a progressive dormancy process by monitoring the power consumption change in real time; and training a reinforcement learning scheduling strategy by taking the queue length of the hot resource pool, the recovery time of the cold resource pool and the calling frequency of the idle resource pool as input. According to the invention, intelligent cyclic scheduling of Kubernetes cluster resources is realized, the resource utilization rate is improved, the energy consumption cost is reduced, the service continuity is guaranteed, and the intelligent level of scheduling decision is improved.

The invention relates to the technical field of metal rolling linear control, in particular to a linear repetition process sliding mode control method and system based on event triggering. According to the control method, a linear repetitive process model, a cyclic scheduling mechanism, a dynamic event triggering mechanism and a sliding mode control strategy are combined, so that the problem that stability, real-time performance and energy consumption are difficult to consider under the scenes of strong channel coupling, large mismatching disturbance, high communication load, internal and external dual-dimensional dynamic coupling of the channel and the like in a traditional control method is solved; and the stability, the control precision and the communication efficiency of the repeated industrial process in a complex environment are improved.

The invention relates to the technical field of water conservancy and hydropower engineering, discloses a method and a system for real-time scheduling of earthwork in a construction period based on rule coupling, and aims to solve the problem that an existing earthwork balancing method has a weak practical guiding effect on a construction process. And real-time state information of each link is collected in real time, and according to the global dispatching matching matrix and a preset earth-rock dispatching macroscopic rule, multi-stage material source distribution taking direct dam-climbing priority and cost optimization as principles and transport vehicle assignment taking vehicle utilization rate balance as a target are dynamically executed. According to the method, static planning and dynamic response are combined, real-time and intelligent earthwork scheduling is realized, the direct dam climbing rate and the transportation resource utilization efficiency are effectively improved, and the comprehensive construction cost is reduced.

Methods of data processing and data processing apparatuses are disclosed. When performing data processing operations in response to program instructions, a scheduler is operated to support cooperative scheduling of multiple processes with reference to a circular scheduler queue comprising one or more queue elements. Each process of the multiple processes, when active as a currently executing process, is arranged to yield execution at a yield instruction to allow a next process of the multiple processes to continue execution. When the currently executing process yields, the scheduler initiates execution of the next process by reading context data for the next process from a queue element pointed to by a schedule pointer and writes the context data for the currently executing process to the same queue element, before advancing the schedule pointer. Lightweight cooperative scheduling is thereby supported.

The invention relates to a sparse convolutional neural network accelerator, a data processing method and a medium. The accelerator architecture comprises a convolution accelerator module, a weight compression module, a computing unit array and a load balancing module. A compression weight vector and a weight mask are generated through a weight compression technology, so that the data storage and transmission quantity is remarkably reduced; a dynamic skipping mechanism based on mask and zero value detection is introduced into a calculation unit, and invalid calculation is effectively eliminated; and a weight weighted polling and characteristic value cyclic scheduling dual load balancing strategy is adopted, so that load balancing of each unit of the calculation array is ensured, and the hardware utilization rate is greatly improved. The sparse convolutional neural network accelerator provided by the invention supports efficient sparse convolutional calculation, reduces the calculation amount of the convolutional neural network, is suitable for edge calculation, automatic driving and other scenes, the actual measurement shows that the utilization rate of a calculation unit can reach 92%, the performance is improved by 2.78 times compared with that of a traditional scheme, and meanwhile, the power consumption and storage bandwidth requirements are remarkably reduced.

The invention provides a DAG (Directed Acyclic Graph) task scheduling control method based on neural network scoring, which belongs to the technical field of task scheduling control and comprises the following steps of: 1, constructing a DAG task dependency graph according to tasks and dependency relationships thereof; 2, extracting multi-dimensional task features from the DAG task dependency graph and system historical data; 3, inputting core task characteristics, and outputting task scheduling priority scores; 4, performing resource availability judgment on the task; step 5, dynamically adjusting the maximum degree of parallelism of the task; step 6, when the scheduling task is executed, performing task execution monitoring and state updating at the same time; and 7, cyclically scheduling and repeatedly executing the processes from the step 2 to the step 6 until all tasks in the DAG task dependency graph are completed. By constructing a multi-dimensional task feature system, designing a hybrid neural network scoring model and dynamically optimizing a scheduling strategy and the degree of parallelism, efficient, intelligent and adaptive task scheduling oriented to a complex distributed environment is realized.

This invention belongs to the field of mobile communication technology and discloses a service flow scheduling method for high-speed rail millimeter-wave communication scenarios assisted by unmanned aerial vehicles (UAVs). By configuring system parameters such as node location and link parameters, it acquires information such as mobile relay location, candidate link status, and service requirements in real time. It calculates the propagation distance based on the node and mobile relay locations, and uses link parameters to obtain the average load per time slot within a frame. Then, it calculates the minimum number of time slots required for each mobile relay to complete the service through candidate links. Links exceeding the remaining budget of the resource pool are then eliminated to form a feasible candidate set. This scheme can accurately select effective links, avoid resource conflicts and underutilization, and ensure reasonable resource allocation. Based on a scoring system, it selects the optimal link and dynamically updates the candidate set, taking into account both service priority and starvation status. This avoids the problem of high-priority services being squeezed out and weak link services being starved. Furthermore, frame-level cyclic scheduling reduces algorithm complexity and meets the real-time scheduling requirements of high-speed rail scenarios.

The invention discloses a batch job scheduling method, device and equipment, a medium and a program product. The method comprises the following steps: dividing a target task into M jobs to be scheduled according to a task granularity division rule; taking the M jobs to be scheduled as current alternative jobs to be scheduled; extracting a to-be-scheduled job meeting a scheduling condition from the current alternative to-be-scheduled jobs as a current target to-be-scheduled job according to a dependency relationship between the current alternative to-be-scheduled jobs; performing batch scheduling on the current target to-be-scheduled job through a distributed batch framework; and if the M to-be-scheduled jobs are not completely scheduled, taking the remaining to-be-scheduled jobs in the M to-be-scheduled jobs as current alternative to-be-scheduled jobs, and repeating the operation until the M to-be-scheduled jobs are completely scheduled. Through reasonable task granularity division, accurate dependency relationship processing and an efficient cycle scheduling mechanism, the problems of scheduling delay, blocking, dependency conflicts and the like in an existing batch job scheduling scheme are solved.