Enabling Fog Computing for Industrial Automation Through Time-Sensitive Networking (TSN)

Pop, Paul; Raagaard, Michael Lander; Gutiérrez, Marina; Steiner, Wilfried

doi:10.1109/mcomstd.2018.1700057

Cited by 111 publications

(59 citation statements)

References 9 publications

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“…Further related work that is complementary to our study has been conducted by Pop et al [11], Hackel et al [12], Herlich et al [13], Nayak et al [14]- [16], and Kobzan et al [17].…”

Section: B Related Workmentioning

confidence: 54%

Reconfiguration Algorithms for High Precision Communications in Time Sensitive Networks

Nasrallah

Venkatraman

Thyagaturu

et al. 2019

2019 IEEE Globecom Workshops (GC Wkshps)

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As new networking paradigms emerge for different networking applications, e.g., cyber-physical systems, and different services are handled under a converged data link technology, e.g., Ethernet, certain applications with mission critical traffic cannot coexist on the same physical networking infrastructure using traditional Ethernet packet-switched networking protocols. The IEEE 802.1Q Time Sensitive Networking (TSN) task group is developing protocol standards to provide deterministic properties on Ethernet based packet-switched networks. In particular, the IEEE 802.1Qcc, centralized management and control, and the IEEE 802.1Qbv, Time-Aware Shaper, can be used to manage and control scheduled traffic streams with periodic properties along with best-effort traffic on the same network infrastructure. In this paper, we investigate the effects of using the IEEE 802.1Qcc management protocol to accurately and precisely configure TAS enabled switches (with transmission windows governed by gate control lists (GCLs) with gate control entries (GCEs)) ensuring ultra-low latency, zero packet loss, and minimal jitter for scheduled TSN traffic. We examine both a centralized network/distributed user model (hybrid model) and a fullydistributed (decentralized) 802.1Qcc model on a typical industrial control network with the goal of maximizing scheduled traffic streams.

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“…Further related work that is complementary to our study has been conducted by Pop et al [11], Hackel et al [12], Herlich et al [13], Nayak et al [14]- [16], and Kobzan et al [17].…”

Section: B Related Workmentioning

confidence: 54%

Reconfiguration Algorithms for High Precision Communications in Time Sensitive Networks

Nasrallah

Venkatraman

Thyagaturu

et al. 2019

2019 IEEE Globecom Workshops (GC Wkshps)

View full text Add to dashboard Cite

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“…To make MEC an enabler for latency-critical IIoT applications, time-sensitive networking (TSN) 2 is a vital solution. [193] proposed TSN-based configuration architectures of MEC that can support real-time IIoT applications. Considering system resources, [155] reported that enabling MEC in IIoT systems can improve the system efficiency by jointly designing resource allocation and offloading based on an auction-based method where both claimed bids and asked prices were given by the MEC servers.…”

Section: ) Industrial Internetmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

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“…The work in [27] presents a heuristic incremental scheduling algorithm to minimize the impact of reconfiguring schedules on existing flows. The work in [28] proposes a configuration agent architecture and a simple scheduling heuristic such that the schedules of dynamic flows can be quickly generated and configured. However, these algorithms do not consider the capacity limitation of network switches.…”

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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Enabling Fog Computing for Industrial Automation Through Time-Sensitive Networking (TSN)

Cited by 111 publications

References 9 publications

Reconfiguration Algorithms for High Precision Communications in Time Sensitive Networks

Reconfiguration Algorithms for High Precision Communications in Time Sensitive Networks

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

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

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