12 results about "List scheduling" patented technology

The invention discloses an efficient task scheduling method based on distributed collaboration, and belongs to the technical field of distributed computing. Aiming at the problems of non-uniform resource allocation, insufficient task type difference adaptation, lack of multi-task dependence global optimization and the like existing in the existing scheduling strategy, the invention provides the following technical scheme: firstly, classifying tasks based on calculation characteristics and establishing a multi-dimensional resource index; secondly, performing dynamic weight evaluation on the heterogeneous resources by adopting an optimal worst weighting method (BWM), and constructing a task-resource matching matrix; a task dependency relationship is modeled through a directed acyclic graph (DAG), and a global priority sequence is generated in combination with a list scheduling algorithm; and finally, single-task optimal node matching is realized by adopting an elimination selection method, and cooperative scheduling is performed on multiple tasks by applying an arithmetic optimization algorithm (AOA). According to the method, accurate resource matching of a calculation-intensive task and a data-intensive task is realized through three technical dimensions of task feature perception, resource dynamic adaptation and dependency relationship collaborative optimization.

The invention discloses a coarse-grained reconfigurable architecture cyclic mapping method and device based on a graph neural network, and relates to the technical field of computer reconfigurable computing. The method comprises the following steps: based on a priority prediction model, performing list scheduling according to a data flow diagram and hardware information to obtain a list scheduling table; performing modular scheduling according to the data flow diagram and the hardware information; pre-scheduling the data flow diagram on the basis of a list scheduling table according to the modular data flow diagram and the time extension coarse-grained array to obtain a time step attribute data flow diagram; obtaining a mapping result by using a mapping algorithm based on pattern diagram matching according to the time step attribute data flow diagram on the basis of the time extension coarse-grained array; based on the hardware information, processing unit isomorphism verification is carried out according to the mapping result; and compiling the verified mapping result to obtain an executable configuration information file. The invention provides an efficient and accurate coarse-grained reconfigurable architecture cyclic mapping method based on a graph neural network.

The invention discloses an equipment access scheduling method and related equipment. The method comprises the following steps: receiving a registration request sent by a device end; determining a target access point for the device end by using the scheduling decision information, and sending related information of the target access point to the device end; wherein the scheduling decision information is cached in the Internet of Things cloud platform, the scheduling decision information comprises a candidate access point list corresponding to a plurality of attribute scheduling types, and the scheduling decision information is obtained by analyzing connection related data of a plurality of access points; and accessing the device end to the Internet of Things cloud platform through the related information of the target access point. According to the scheme, the stability and success rate of connection between the equipment end and the Internet of Things cloud platform can be improved.

This invention discloses a scheduling method for dependent tasks based on a combination of DRL and HSA, including: a step of constructing a directed unordered graph based on dependent tasks; a reinforcement learning-based dependent task scheduling algorithm designed for edge computing scenarios, modeling the dependent task scheduling problem as a Markov decision process, modeling and representing the edge computing scenario and dependent tasks, and combining deep reinforcement learning algorithms, using multilayer perceptrons and cross-attention mechanisms to provide execution strategies and node selection strategies based on task execution paths; for edge computing scenarios where there are large differences in computing power due to node heterogeneity, a heuristic scheduling algorithm based on list scheduling is designed. In scenarios where there are large differences in node computing power, especially where nodes with hardware accelerators cause large differences in the execution time of dependent tasks on different nodes, the algorithm provides task scheduling priorities based on task-node matching differences, and selects execution nodes based on these priorities.

A terminal includes a receiving unit that receives first system information from a base station; and a control unit that acquires, from the first system information, a first list, a second list, and a third list, the first list scheduling second system information other than the first system information, and determines, based on the second list and the third list, a start position of a window for acquiring the second system information, wherein the receiving unit receives, from the base station, the second system information in the window.

The present invention belongs to the technical field of real-time embedded systems, and specifically relates to a heuristic task scheduling method based on DAG. The present invention divides the nodes of DAG into several subsets, then schedules each node subset, and merges the scheduling of all node subsets into scheduling for all task nodes on the entire DAG. The method can complete static scheduling of the system during the compilation phase of the design of an embedded system with a DAG structure. By using the scheduling method for tasks on DAG proposed by the present invention, the average response time of the system is effectively reduced compared to the classic Graham list scheduling algorithm, and embedded systems with limited computing resources can increase the number of tasks completed per unit time and complete tasks with as few running cores as possible, thereby avoiding resource waste.

The application is an automated laboratory non-deadlock dynamic scheduling method based on Monte Carlo tree search, and belongs to the technical field of industrial intelligent scheduling. The method models the tasks of the automated laboratory as a directed acyclic graph, searches the task scheduling decision sequence for the graph by using the Monte Carlo tree search method, the state of the tree node includes the state of whether the task is executed and the state of whether the device is occupied, the action of the edge represents scheduling which task to execute on which device, the variables of the reward value, the access frequency and the number of deadlocks are set for the tree node, the improved action selection function is designed, the branch and bound strategy is used to perform the pruning operation on the expanded child nodes, etc.; the list scheduling mode is used for actual scheduling, and the task directed acyclic graph is reconstructed when a dynamic event occurs. The method can not only complete the optimization target of minimizing the maximum task completion time, but also effectively solve the scheduling deadlock problem caused by the blocking of the task to the device and the resource competition.

This application proposes a 5G-TSN scheduling optimization method and apparatus to address the problem of degraded deterministic transmission performance in the fusion of 5G systems and time-sensitive networks caused by the mismatch between gating list scheduling and transmission time interval scheduling at the basic time granularity. The 5G-TSN scheduling optimization method includes the following steps: acquiring network parameters; determining a reference transmission time interval based on the subcarrier spacing; adjusting the original GCL timeslot length to an integer multiple of the reference transmission time interval; determining the target GCL configuration; and distributing the target GCL configuration to the 5G radio access network. By uniformly adjusting the GCL timeslot length to an integer multiple of the TTI, this application achieves precise time alignment between wired and wireless scheduling, significantly reducing data transmission latency and effectively improving radio resource utilization, thereby providing end-to-end deterministic transmission guarantees for 5G-TSN networks.

The invention provides a 5G-TSN scheduling optimization method and device, and solves the problem that the deterministic transmission performance of fusion of a 5G system and a time-sensitive network is reduced due to the fact that gating list scheduling and transmission time interval scheduling are not matched in basic time granularity. The 5G-TSN scheduling optimization method comprises the following steps: acquiring network parameters; determining a reference transmission time interval according to the subcarrier interval, adjusting the GCL original time slot duration to be an integral multiple of the reference transmission time interval, and determining a target GCL configuration; and issuing the target GCL configuration to a 5G wireless access network. According to the method and the device, the GCL time slot length is uniformly adjusted to be the integral multiple of the TTI, so that accurate time alignment of wired side and wireless side scheduling is realized, data transmission delay can be remarkably reduced, the wireless resource utilization rate can be effectively improved, and end-to-end deterministic transmission guarantee is provided for a 5G-TSN network.

The invention relates to an adaptive scheduling system for big data analysis tasks, and the system comprises a task management module which is used for receiving online tasks and distributing unique identifiers for the online tasks, so as to form a to-be-completed task set; the execution node management module is used for acquiring resources of execution nodes in real time and evaluating the fitness of online tasks and the execution nodes according to the resources; the scheduling analysis module is used for calculating a response time matrix of each online task of the to-be-completed task set on different execution nodes according to the fitness, and generating a task-execution node preference list; the scheduling decision module is used for generating a scheduling decision according to the sequence of the task-execution node preference list; and the data migration module is used for migrating the data source corresponding to the required data set to the data set stored in the target execution node when the required data set of the online task is not stored in the target execution node. By adopting the system, the task-execution node combination can be selected according to the quantized data, and the overall response time of the task is effectively shortened.

The embodiment of the invention provides a workflow white list scheduling method and system and a storage medium, and belongs to the technical field of workflow scheduling. The method comprises the following steps: traversing metadata of a workflow scheduling system, identifying task nodes and analyzing a dependency relationship to construct a global task dependency graph; receiving white list configuration submitted by a user, associating a target task node with a basic priority weight and mapping the target task node and the basic priority weight to a global task dependency graph; executing downstream traversal in the global task dependency graph, and accumulating the basic priority weight of the target task node to the corresponding upstream task node to determine a global effective priority weight; and calling a scheduler to sort the task nodes in the runnable state, and allocating computing resources according to the global effective priority weight. According to the scheme of the invention, the priority guarantee of cross-DAG key tasks and the reasonable allocation of computing resources are realized, so that the certainty of overall scheduling and the execution efficiency of service links are improved.

The application belongs to the technical field of compiler optimization, and specifically discloses a compiler back-end instruction scheduling optimization method and system based on a Mamba state space model, which comprises the following steps: obtaining an instruction sequence in a compiler back-end machine basic block, constructing a scheduling directed acyclic graph, extracting a multi-dimensional feature vector and an instruction embedding matrix of each instruction, and encoding a target CPU micro-architecture descriptor into a global context vector; inputting the instruction embedding matrix into a Mamba selective state space model to output a scheduling priority score of each instruction; iteratively decoding in each scheduling slot to generate a legal instruction scheduling arrangement that satisfies all data dependency constraints; feeding the legal instruction scheduling arrangement back to the compiler back-end and adjusting the priority selection process in the original list scheduling to realize instruction scheduling optimization. According to the technical scheme, the instruction scheduling problem is formalized into a sequence-to-sequence optimization problem, a high-quality instruction scheduling arrangement is obtained, and the real-time requirement in the compilation period is met.