Enhancing Throughput for TTEthernet via Co-optimizing Routing and Scheduling: An Online Time-Varying Graph-based Method

TL;DR

This work tackles online routing and scheduling in Time-Triggered Ethernet (TTEthernet) where flows arrive and depart dynamically. It introduces the Time-Slot Expanded Graph (TSEG) to compactly model temporal-spatial resources and casts scheduling as edge selection, enabling a formal ILP bound and a fast, online JRAS-TSEG algorithm that co-optimizes routing and transmission timing. The dynamic edge-weighting scheme reveals conflict relationships among flows, and the minimum-weight path strategy minimizes adverse impacts on future requests; empirically, the method runs >$400$× faster than a standard ILP solver with only a $2\%$ optimality gap and schedules at least 18% more flows than baselines on typical industrial topologies. These results demonstrate significant throughput gains and scalable online performance, making TTEthernet more suitable for agile applications like smart factories, autonomous systems, and satellite networks. The approach thus provides a practical, theoretically-grounded mechanism to achieve high-quality, fast online routing and scheduling in deterministic networks.

Abstract

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%.

Figures (11)

• Figure 1: An example of the effect of this coupling relationship on the completed number of transmission requests.
• Figure 2: An example of the hyper-period in an online scenario
• Figure 3: Centralized Configuration and Management Model
• Figure 4: Time-slot expanded graph model
• Figure 5: Example of the JRS-TSEG algorithm, where the input is two flows with periods of 4 time slots and 2 time slots, and the output is a path represented by dashed lines.