Edge Powered Industrial Control: Concept for Combining Cloud and Automation Technologies

Pallasch, Christoph; Wein, Stephan; Hoffmann, Nicolai; Obdenbusch, Markus; Buchner, Tilman; Waltl, Josef; Brecher, Christian

doi:10.1109/edge.2018.00026

Cited by 30 publications

(12 citation statements)

References 11 publications

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“…The approach also enables the integration of CPS with Cloud and EC. Pallasch et al (2018) introduced a concept to utilise Cloud and EC for industrial control. They refer to the devices connected to field tier devices as Edge Devices.…”

Section: Related Workmentioning

confidence: 99%

Real-time edge framework (RTEF): task scheduling and realisation

Gezer

Wagner

2021

J Intell Manuf

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With the big success of the Cloud Computing, or the Cloud, new research areas appeared. Edge Computing (EC) is one of the recent paradigms that is expected to overcome the Quality of Service (QoS) and latency issues caused by the best-effort behaviour of the Cloud. EC aims to bring the computation power close to the end devices as much as possible and reduce the dependency to the Cloud. Bringing computing power close to the source also enables real-time applications. In this paper, we propose a novel software reference architecture for Edge Servers, which is operating system (OS) and hardware-agnostic. Edge Servers can collaborate and execute (near) real-time tasks on time, either by downscaling or scheduling them according to their deadlines or offloading them to other Edge Servers in the network. Decision making for offloading, resource planning, and task scheduling are challenging problems in decentralized systems. The paper explains how resource planning and task scheduling can be overcome with software approach. Finally, the article realises the architecture as a framework, called Real-Time Edge Framework (RTEF) and validates its correctness with a use case.

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Section: Related Workmentioning

confidence: 99%

Real-time edge framework (RTEF): task scheduling and realisation

Gezer

Wagner

2021

J Intell Manuf

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“…Edge computing has gained attention in the last decade and the industry is using it as a valuable approach for solving different problems. In this regard, researchers in [26] present a platform concept, which combines cloud computing and industrial control using edge devices realized for an automation cell. The target is lying behind the approach of industrial control of field devices, comprising self-contained systems in a dedicated network for exchanging control information between field devices and control hardware, so as to accomplish process tasks and moving towards a solution based on the continuum cloud to edge.…”

Section: Industrial Applications On the Edgementioning

confidence: 99%

The Extreme Edge at the Bottom of the Internet of Things: A Review

et al. 2019

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resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This general definition shows the power that cloud computing makes available to the Internet of Things. The basic idea is to collect information, usually big amounts of data, in the point of interest (this so-called the edge, where information is generated) and upload it to be properly processed in the cloud, where ideally permanent and enough processing resources are available. This model may work perfectly with some approaches where the energy resource is not a limitation or the time required for any decision does not impose a very fast reaction. However, the cloud computing paradigm applied directly to IoT presents a set of drawbacks regarding latency, bandwidth and storage [10] because of the huge amount of data that have to be uploaded and processed. Due to these limitations, more layers have been proposed during the last years, getting closer to the source of data.

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“…In conclusion, the literature overview shows that there is a thorough analysis of different facets of EC in place as well as approaches for the planning and design of distributed IT infrastructures. There are singular examples [6], [13] of the application of EC in production environments, but they are still scarce. Furthermore, work on planning and design would allow determining an optimal configuration but does not allow a prior evaluation.…”

Section: B It Resource Design/planningmentioning

confidence: 99%

Edge Computing for the Production Industry A Systematic Approach to Enable Decision Support and Planning of Edge

Zietsch

Weinert

Herrmann

et al. 2019

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

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Because the Edge Computing (EC) paradigm allows processing of vast amounts of data in proximity to the respective source, latency and quantity constraints are no longer a limiting factor. That enables the development of novel data-driven applications and the extension of the solutions space for valueadded services in production. The complexity and diversity of factories, combined with the continuing discovery of new datadriven solutions, poses a challenge for practitioners to thoroughly determine where, which, and how data should be processed. This, however, is crucial for deciding how and whether to invest in EC. This paper proposes a multiphase concept for the systematic assessment of whether and where EC is most beneficial in a given production environment. It is comprised of human and machine interpretable functions. Combining multiple functions leads to a data-driven solution, which forms links between the data sources (assets) of a production environment and the desired outcome (goals). Four main criteria for EC are derived to enable the exposure of areas with increased EC potential, forming the baseline for a scoring system. The concept is designed so that its application is feasible within an industrial context. First analyses show the prospect of the approach and suggest potential benefits for providing practical implementation guidance.

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Edge Powered Industrial Control: Concept for Combining Cloud and Automation Technologies

Cited by 30 publications

References 11 publications

Real-time edge framework (RTEF): task scheduling and realisation

Real-time edge framework (RTEF): task scheduling and realisation

The Extreme Edge at the Bottom of the Internet of Things: A Review

Edge Computing for the Production Industry A Systematic Approach to Enable Decision Support and Planning of Edge

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