Multicast-partitioning in Time-triggered Stream Planning for Time-Sensitive Networks

TL;DR

This paper introduces a multicast partitioning technique for Time-triggered Stream Planning in Time-Sensitive Networks, improving traffic schedulability and network throughput by splitting multicast trees into smaller units.

Contribution

The paper presents a novel multicast partitioning method that enhances traffic scheduling and network performance in time-sensitive networks.

Findings

5-15% fewer stream rejections with partitioning

5-125% increase in network throughput

Improved schedulability across different network topologies

Abstract

Multicast allows sending a message to multiple recipients without having to create and send a separate message for each recipient. This preserves network bandwidth, which is particularly important in time-sensitive networks. These networks are commonly used to provide latency-bounded communication for real-time systems in domains like automotive, avionics, industrial internet of things, automated shop floors, and smart energy grids. The preserved bandwidth can be used to admit additional real-time messages with specific quality of service requirements or to reduce the end-to-end latencies for messages of any type. However, using multicast communication can complicate traffic planning, as it requires free queues or available downstream egress ports on all branches of the multicast tree. In this work, we present a novel multicast partitioning technique to split multicast trees into smaller multicast or unicast trees. This allows for a more fine-grained trade-off between bandwidth utilization and traffic scheduling difficulty. Thus, schedulability in dynamic systems can be improved, in terms the number of admitted streams and the accumulated network throughput. We evaluated the multicast partitioning on different network topologies and with three different scheduling algorithms. With the partitioning, 5-15\% fewer streams were rejected, while achieving 5-125\% more network throughput, depending on the scheduling algorithm.