PTAS: PIFO-Based Time-Aware Shaper for Massive Concurrent Flows in Time-Sensitive Networks

Time-sensitive networking (TSN) specifies a time-aware shaper (TAS) to enhance the real-time support of Ethernet for time-triggered traffic. In TAS, flow/frame isolation is necessary for obtaining a deterministic queuing order, which is the key to achieving determinism. However, this isolation is difficult to fulfill when accessing a large number of concurrent flows generated by industrial devices without TSN capabilities due to the uncontrollable sending times and a limited number of egress queues in TAS. In this paper, a novel TAS framework is designed for massive concurrent flows, named push-in-first-out (PIFO) based time-aware shaper (PTAS). Specifically, the PTAS takes advantage of a PIFO queue to buffer and sort all time-triggered frames based on expected sending time (EST), while an ephemeral memory EST allocation algorithm is proposed to obtain ESTs that can ensure a deterministic queuing order. Through frame sorting rather than conventional isolation, the PTAS efficiently provides a deterministic transmission guarantee for massive concurrent flows. Furthermore, the deterministic flow scheduling constraints for the PTAS are derived, based on which the PTAS is proven to have better schedulability than conventional TAS. In addition to simulation, the PTAS is also implemented on a TSN testbed for evaluation. Extensive experimental results demonstrate that the PTAS can reduce up to 37.0% maximum end-to-end latency, 98.8% jitter, and save 40.1% queue resources in high-concurrency scenarios while scheduling up to 15.5% more flows as compared with conventional TAS.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Network Science and Engineering
Published
2024-10-24
DOI
https://doi.org/10.1109/tnse.2024.3486038
Citations
5
Primary Topic
Network Time Synchronization Technologies
Type
article
Field-Weighted Citation Impact
1.74
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PTAS: PIFO-Based Time-Aware Shaper for Massive Concurrent Flows in Time-Sensitive Networks

Kai Gong, Jie Ren, Weiliang Chen, Dong Yang et al.
5 citations
IEEE Transactions on Network Science and Engineering
Network Time Synchronization Technologies
1.74
article

PTAS: PIFO-Based Time-Aware Shaper for Massive Concurrent Flows in Time-Sensitive Networks

Kai Gong, Jie Ren, Weiliang Chen, Dong Yang, Wen Wu, Hongke Zhang, Weiting Zhang
article en
5 citations

Abstract

Time-sensitive networking (TSN) specifies a time-aware shaper (TAS) to enhance the real-time support of Ethernet for time-triggered traffic. In TAS, flow/frame isolation is necessary for obtaining a deterministic queuing order, which is the key to achieving determinism. However, this isolation is difficult to fulfill when accessing a large number of concurrent flows generated by industrial devices without TSN capabilities due to the uncontrollable sending times and a limited number of egress queues in TAS. In this paper, a novel TAS framework is designed for massive concurrent flows, named push-in-first-out (PIFO) based time-aware shaper (PTAS). Specifically, the PTAS takes advantage of a PIFO queue to buffer and sort all time-triggered frames based on expected sending time (EST), while an ephemeral memory EST allocation algorithm is proposed to obtain ESTs that can ensure a deterministic queuing order. Through frame sorting rather than conventional isolation, the PTAS efficiently provides a deterministic transmission guarantee for massive concurrent flows. Furthermore, the deterministic flow scheduling constraints for the PTAS are derived, based on which the PTAS is proven to have better schedulability than conventional TAS. In addition to simulation, the PTAS is also implemented on a TSN testbed for evaluation. Extensive experimental results demonstrate that the PTAS can reduce up to 37.0% maximum end-to-end latency, 98.8% jitter, and save 40.1% queue resources in high-concurrency scenarios while scheduling up to 15.5% more flows as compared with conventional TAS.

IEEE Transactions on Network Science and EngineeringVol. 12(1)
Beijing Jiaotong University (CN), Peng Cheng Laboratory (CN), Tsinghua University (CN)
Openalex Percentile: Top 14%
Network Time Synchronization Technologies
1.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.