Name
Abstract | ||
|---|---|---|
Traditional field bus cannot meet the demand of modern trains that generate increasing amounts of control and diagnostic data. To support these communications with real-time requirements, time-triggered network is a promising technology as real-time frames are transmitted according to a pre-computed schedule. However, a unique challenge for modern trains is dynamic train inauguration, where the pre-computed schedule needs to be reconfigured online due to network topology changes. This paper presents a novel scheduling approach specifically designed for the dynamic schedule generation. The novelty of our approach is two-fold. In contrast to the traditional scheduling approaches that rely on time-consuming solvers to generate schedules offline, our scheduler features efficient heuristics-based algorithms. Moreover, our scheduling algorithm is cognizant of the interdependence between bandwidth and memory constraints in time-triggered network switches with shared buffers. Simulation results show that our scheduling algorithm has better scheduling ability than the myopic approach and SMT-based approach when schedules are required to be generated rapidly for train inauguration. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BDCloud.2018.00085 | 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) |
Keywords | Field | DocType |
Industrial Ethernet, Real-Time Scheduling, Train Control Networks | Scheduling (computing),Computer science,Network switch,Network topology,Ethernet,Heuristics,Schedule,Industrial Ethernet,Train,Multimedia,Distributed computing | Conference |
ISSN | ISBN | Citations |
2158-9178 | 978-1-7281-1141-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinghan Yu | 1 | 3 | 1.78 |
| Wang Tian | 2 | 17 | 15.16 |
| Xi-Bin Zhao | 3 | 290 | 30.98 |
| Hai Wang | 4 | 19 | 11.58 |
| Yue Gao | 5 | 3 | 0.74 |
| Chenyang Lu | 6 | 6474 | 385.38 |
| Ming Gu | 7 | 554 | 74.82 |