Time-sensitive networking (TSN), an emerging network technology, requires high-performance scheduling mechanisms to deliver deterministic service in Industry 5.0. Cyclic queuing and forwarding (CQF) is launched to simplify the configuration complexity of the early stage mechanism time-aware shaper in TSN flow scheduling. Previous CQF studies adopt an inflexible incremental flow scheduling scheme, which consists of flow sorting, offset search, and resource judgment. However, we observe that flow sorting and offset search are mutually interdependent. The offset of a flow helps determine the resource status on the flow path, which can guide flow sorting. By utilizing the interaction between flow and offset, we design a novel scheduling approach that achieves high scheduling performance and time efficiency. Specifically, the proposed approach combines flow sorting and offset search together to select flow and its offset (i.e., ( flow, offset )) simultaneously. To effectively determine the selecting priority and select the potential optimal flow-offset combination, we define a unified metric, $mapping\,score$ , to quantify the schedulability of different flow and offset combinations. The extensive experiments demonstrate that the scheduling success rate of our proposed approach is on average 31.69% higher than the baseline and 4.57% higher than the state-of-the-art flow judgement approach (FLJ) method. Moreover, it outperforms the state-of-art FLJ method by 7.62% in large-scale linear topologies, indicating its great scalability in different network scales and complex topologies.

中文翻译：

---

MSS：利用映射分数进行时间敏感网络中的 CQF 开始时间规划

时间敏感网络 (TSN) 是一种新兴的网络技术，需要高性能的调度机制来提供工业 5.0 中的确定性服务。推出循环排队转发（CQF），简化TSN流调度中前期机制时间感知整形器的配置复杂度。以往的CQF研究采用了一种不灵活的增量流调度方案，由流排序、偏移量搜索和资源判断组成。然而，我们观察到流排序和偏移搜索是相互依赖的。流的偏移量有助于确定流路径上的资源状态，可以指导流排序。通过利用流量和偏移量之间的相互作用，我们设计了一种新颖的调度方法，可实现高调度性能和时间效率。具体来说， 流，偏移 )) 同时。为了有效地确定选择优先级并选择潜在的最优流抵消组合，我们定义了一个统一的度量，$映射\,分数$ ，以量化不同流量和偏移量组合的可调度性。大量实验表明，我们提出的方法的调度成功率平均比基线高 31.69%，比最先进的流判断方法 (FLJ) 方法高 4.57%。此外，它在大规模线性拓扑中的性能优于最先进的 FLJ 方法 7.62%，表明其在不同网络规模和复杂拓扑中具有强大的可扩展性。