Genetic Algorithm for Scheduling Time-Triggered Traffic in Time-Sensitive Networks

Pahlevan, Maryam; Obermaisser, Roman

doi:10.1109/etfa.2018.8502515

Cited by 79 publications

(23 citation statements)

References 6 publications

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“…If the network is involved, both the network and the task schedules have to be coordinated. One solution to address task and TSN network scheduling has already been investigated in [ 54 ]. In future works, we will elaborate on the subject matter in more detail.…”

Section: Binding Implementation For Real-time Systemsmentioning

confidence: 99%

A Service-Oriented Real-Time Communication Scheme for AUTOSAR Adaptive Using OPC UA and Time-Sensitive Networking

Arestova

Martin

Hielscher

et al. 2021

Sensors

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The transportation industry is facing major challenges that come along with innovative trends like autonomous driving. Due to the growing amount of network participants, smart sensors, and mixed-critical data, scalability and interoperability have become key factors of cost-efficient vehicle engineering. One solution to overcome these challenges is the AUTOSAR Adaptive software platform. Its service-oriented communication methodology allows a standardized data exchange that is not bound to a specific middleware protocol. OPC UA is a communication standard that is well-established in modern industrial automation. In addition to its Client–Server communication pattern, the newly released Publish–Subscribe (PubSub) architecture promotes scalability. PubSub is designed to work in conjunction with Time-Sensitive Networking (TSN), a collection of standards that add real-time aspects to standard Ethernet networks. TSN allows services with different requirements to share a single physical network. In this paper, we specify an integration approach of AUTOSAR Adaptive, OPC UA, and TSN. It combines the benefits of these three technologies to provide deterministic high-speed communication. Our main contribution is the architecture for the binding between Adaptive Platform and OPC UA. With a prototypical implementation, we prove that a combination of OPC UA Client–Server and PubSub qualifies as a middleware solution for service-oriented communication in AUTOSAR.

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Section: Binding Implementation For Real-time Systemsmentioning

confidence: 99%

A Service-Oriented Real-Time Communication Scheme for AUTOSAR Adaptive Using OPC UA and Time-Sensitive Networking

Arestova

Martin

Hielscher

et al. 2021

Sensors

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“…A heuristic list scheduler for joint communication scheduling and network routing is proposed in [54], where multi-cast traffic and application distribution are allowed, and bandwidth is optimized. The same problem is addressed in [55] where a genetic algorithm is employed and in [23], where multiple traffic types are considered.…”

Section: Related Workmentioning

confidence: 99%

Communication Scheduling for Control Performance in TSN-Based Fog Computing Platforms

Barzegaran

Pop

2021

IEEE Access

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“…The work in [25] proposes a K-shortest path heuristic and a meta-heuristic randomized adaptive search procedure to solve the joint routing and scheduling problem for time-triggered traffic, and at the same time reduce the end-to-end delays of audio-video-bridging traffic. The work in [26] develops two algorithms to schedule multicast time-triggered flows: the first algorithm is based on genetic algorithm and generates schedules under real-time constraints; the second one is based on fixed routings and implemented as a list scheduler. The work in [27] presents a heuristic incremental scheduling algorithm to minimize the impact of reconfiguring schedules on existing flows.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Scheduling of Massive Data in Time Sensitive Networks With a Limited Number of Schedule Entries

et al. 2020

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Time sensitive networks support deterministic schedules over Ethernet networks. Due to their high determinism, high reliability and high bandwidth, they have been considered as a good choice for the backbone network of industrial internet of things. In industrial applications, the backbone network connects multiple industrial field networks together and has to carry massive real-time packets. However, the off-theshelf time-sensitive network (TSN) switches can deterministically schedule no more than 1024 real-time flows due to the limited number of schedule table entries. The excess real-time flows have to be delivered by best-effort services because the switch only supports the two scheduling services. The best-effort services can reduce average delay, but cannot guarantee the hard real-time constraints of industrial applications. To make the limited number of schedule table entries support more real-time flows, first, we relax scheduling rules to reduce the requirement for schedule table entries and formulate the process of transmitting packets as a satisfiability modulo theories (SMT) specification. Then, we divide the SMT specification into multiple optimization modulo theories (OMT) specifications so that the execution time of solvers can be reduced to an acceptable range. Second, we propose fast heuristic algorithms that combine schedule tables and packet injection control to eliminate scheduling conflicts. Finally, we conduct extensive evaluations. The evaluation results indicate that, compared to existing algorithms, our proposed algorithm requires only one-twentieth the number of schedule entries to schedule the same flow set. INDEX TERMS Industrial Internet of Things, massive data, real-time scheduling, time sensitive networks.

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Genetic Algorithm for Scheduling Time-Triggered Traffic in Time-Sensitive Networks

Cited by 79 publications

References 6 publications

A Service-Oriented Real-Time Communication Scheme for AUTOSAR Adaptive Using OPC UA and Time-Sensitive Networking

A Service-Oriented Real-Time Communication Scheme for AUTOSAR Adaptive Using OPC UA and Time-Sensitive Networking

Communication Scheduling for Control Performance in TSN-Based Fog Computing Platforms

Real-Time Scheduling of Massive Data in Time Sensitive Networks With a Limited Number of Schedule Entries

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