时间敏感网络业务流动态调度场景中的批量重配置算法

工业自动化领域广泛使用时间敏感网络技术. 该领域业务流的调度方式主要包含静态调度和动态调度. 静态调度一次计算所有业务流, 可以最大程度节省链路和时间资源, 但是计算时间长, 无法灵活处理新增业务流. 动态调度以增量的形式计算新增业务流, 计算时间短, 但是资源分配不够合理, 会产生时隙碎片. 全局流重配置机制可以定期对网络中所有业务流进行重新规划, 来优化链路和时间资源的分配, 但该机制只适用于拥有较少业务流的小型网络, 业务流数量的增多会引起计算时间的急剧增长, 影响后续到来的业务流. 本文在现有动态调度算法的基础上, 设计了批量重配置算法. 该算法给出了新的评价指标——网络吞吐率, 并在满足动态调度秒级响应时间的情况下, 定期重配置网络中的部分业务流, 优化网络资源配置. 此外, 算法给出了重配置业务流的选取标准, 并优化了流的路径选择标准和传输开始时间计算方式. 本文针对原算法和增加了批量重配置机制的改进算法进行了仿真实验, 实验结果表明, 改进算法可以在拥有数千条业务流的大型网络运行, 并在网络吞吐率和调度成功的流数量方面有16.5%和5.5%的提升, 同时保证了算法的秒级计算时间.

Batch Reconfiguration Algorithm in Dynamic Flow Scheduling Scenario of Time-sensitive Network

Time-sensitive network technologies are widely used in industrial automation. The flow scheduling methods in this field mainly include static and dynamic scheduling. Static scheduling computes all flows at a time, which can save link and time resources to the greatest extent but has the disadvantages of long computation time and lack of flexibility to handle new flows. Dynamic scheduling computes new flows incrementally, with short computation time but insufficient resource allocation, resulting in time slot fragmentation. The global flow reconfiguration mechanism can periodically replan all flows in the network to optimize the allocation of link and time resources. However, this mechanism only applies to small networks with fewer flows, and an increase in the flow number can cause a sharp increase in computation time, affecting subsequent flows. This study designs a batch reconfiguration algorithm based on the existing dynamic scheduling algorithm. This algorithm provides a new evaluation indicator: network throughput. It can regularly reconfigure some flows to optimize network resource allocation while meeting the dynamic scheduling second-level response time requirement. In addition, the algorithm gives reconfigured flow selection standards and optimizes flow path selection standards and transmission start time calculation. This study conducts simulation experiments on the original and improved algorithm with the batch reconfiguration mechanism. The experimental results show that the improved algorithm can run in large networks with thousands of flows and have a 16.5% and 5.5% improvement in network throughput and the number of successfully scheduled flows while ensuring the second-level calculation time of the algorithm.