Time-Triggered Ethernet (TTEthernet) has been widely applied in many scenarios such as industrial internet, automotive electronics, and aerospace, where offline routing and scheduling for TTEthernet has been largely investigated. However, predetermined routes and schedules cannot meet the demands in some agile scenarios, such as smart factories, autonomous driving, and satellite network switching, where the transmission requests join in and leave the network frequently. Thus, we study the online joint routing and scheduling problem for TTEthernet. However, balancing efficient and effective routing and scheduling in an online environment can be quite challenging. To ensure high-quality and fast routing and scheduling, we first design a time-slot expanded graph (TSEG) to model the available resources of TTEthernet over time. The fine-grained representation of TSEG allows us to select a time slot via selecting an edge, thus transforming the scheduling problem into a simple routing problem. Next, we design a dynamic weighting method for each edge in TSEG and further propose an algorithm to co-optimize the routing and scheduling. Our scheme enhances the TTEthernet throughput by co-optimizing the routing and scheduling to eliminate potential conflicts among flow requests, as compared to existing methods. The extensive simulation results show that our scheme runs >400 times faster than standard solutions (i.e., ILP solver), while the gap is only 2% to the optimally scheduled number of flow requests. Besides, as compared to existing schemes, our method can improve the successfully scheduled number of flows by more than 18%.

翻译：时间触发以太网（TTEthernet）已广泛应用于工业互联网、汽车电子、航空航天等场景，其离线路由与调度方法已得到大量研究。然而，在智能工厂、自动驾驶、卫星网络切换等敏捷场景中，传输请求频繁加入和离开网络，预定的路由与调度方案难以满足需求。为此，本文研究TTEthernet的在线联合路由与调度问题。然而，在线环境下实现高效且有效的路由与调度平衡极具挑战性。为确保高质量且快速的在线路由与调度，我们首先设计了一种时隙扩展图（TSEG）来建模TTEthernet随时间变化的可用资源。TSEG的细粒度表示允许通过选择边来选择时隙，从而将调度问题转化为简单的路由问题。接着，我们为TSEG中的每条边设计了动态权重方法，并进一步提出了一种协同优化路由与调度的算法。与现有方法相比，该方案通过消除流请求间的潜在冲突，提升了TTEthernet的吞吐量。大量仿真结果表明，我们的方案运行速度比标准求解器（如ILP求解器）快400倍以上，同时成功调度的流请求数量与最优解的差距仅为2%。此外，与现有方案相比，该方法可将成功调度的流数量提升超过18%。