Network Calculus-Based Latency for Time-Triggered Traffic under Flexible Window-Overlapping Scheduling (FWOS) in a Time-Sensitive Network (TSN)

Deterministic latency is an urgent demand to pursue the continuous increase in intelligence in several real-time applications, such as connected vehicles and automation industries. A time-sensitive network (TSN) is a new framework introduced to serve these applications. Several functions are defined in the TSN standard to support time-triggered (TT) requirements, such as IEEE 802.1Qbv and IEEE 802.1Qbu for traffic scheduling and preemption mechanisms, respectively. However, implementing strict timing constraints to support scheduled traffic can miss the needs of unscheduled real-time flows. Accordingly, more relaxed scheduling algorithms are required. In this paper, we introduce the flexible window-overlapping scheduling (FWOS) algorithm that optimizes the overlapping among TT windows by three different metrics: the priority of overlapping, the position of overlapping, and the overlapping ratio (OR). An analytical model for the worst-case end-to-end delay (WCD) is derived using the network calculus (NC) approach considering the relative relationships between window offsets for consecutive nodes and evaluated under a realistic vehicle use case. While guaranteeing latency deadline for TT traffic, the FWOS algorithm defines the maximum allowable OR that maximizes the bandwidth available for unscheduled transmission. Even under a non-overlapping scenario, less pessimistic latency bounds have been obtained using FWOS than the latest related works.

[1]  Paul Pop,et al.  AVB-Aware Routing and Scheduling of Time-Triggered Traffic for TSN , 2018, IEEE Access.

[2]  Luxi Zhao,et al.  Improving Latency Analysis for Flexible Window-Based GCL Scheduling in TSN Networks by Integration of Consecutive Nodes Offsets , 2021, IEEE Internet of Things Journal.

[3]  Silviu S. Craciunas,et al.  Worst-Case Latency Analysis for IEEE 802.1Qbv Time Sensitive Networks Using Network Calculus , 2018, IEEE Access.

[4]  Hai Wan,et al.  Flow Scheduling for Conflict-Free Network Updates in Time-Sensitive Software-Defined Networks , 2021, IEEE Transactions on Industrial Informatics.

[5]  A. Finzi,et al.  Worst-Case Timing Analysis of AFDX Networks With Multiple TSN/BLS Shapers , 2020, IEEE Access.

[6]  Ghaith Bany Hamad,et al.  Routing and Scheduling of Time-Triggered Traffic in Time-Sensitive Networks , 2020, IEEE Transactions on Industrial Informatics.

[7]  Jianqi Shi,et al.  A Feasibility Analysis Framework of Time-Sensitive Networking Using Real-Time Calculus , 2019, IEEE Access.

[8]  Martin Reisslein,et al.  Performance Comparison of IEEE 802.1 TSN Time Aware Shaper (TAS) and Asynchronous Traffic Shaper (ATS) , 2019, IEEE Access.

[9]  Martin Reisslein,et al.  Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research , 2018, IEEE Communications Surveys & Tutorials.

[10]  Norman Finn,et al.  Introduction to Time-Sensitive Networking , 2018, IEEE Communications Standards Magazine.

[11]  Marcel Verhoef,et al.  System architecture evaluation using modular performance analysis: a case study , 2006, International Journal on Software Tools for Technology Transfer.

[12]  Frank Dürr,et al.  Incremental Flow Scheduling and Routing in Time-Sensitive Software-Defined Networks , 2018, IEEE Transactions on Industrial Informatics.

[13]  Zifan Zhou,et al.  Insight into the IEEE 802.1 Qcr Asynchronous Traffic Shaping in Time Sensitive Network , 2019 .

[14]  Yue Gao,et al.  Online Scheduling for Dynamic VM Migration in Multicast Time-Sensitive Networks , 2020, IEEE Transactions on Industrial Informatics.

[15]  Ming Gu,et al.  Adaptive Group Routing and Scheduling in Multicast Time-Sensitive Networks , 2020, IEEE Access.