Workflow management for edge driven manufacturing systems

Derhamy, Hasan; Andersson, Mattias; Eliasson, Jens; Delsing, Jerker

doi:10.1109/icphys.2018.8390805

Cited by 18 publications

(10 citation statements)

References 7 publications

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“…AHF has been developed as an infrastructure for services in various fields of application, initially for IoT integration [25], and a SoS composition model has been proposed with requirements for industrial information distribution using graphs [26]. Using AHF, an approach decentralizing ISA 95 production functions to the lower levels has been presented [27] also utilising edge computing in a service based SoS.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Edge and Cloud Service Integration for Industrial IoT and Production Monitoring Applications of Industrial Cyber-Physical Systems

Hästbacka

Halme

Barna

et al. 2022

IEEE Trans. Ind. Inf.

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Industrial cyber-physical systems rely increasingly on data from IoT devices and other systems as continuously emerging use cases implement new intelligent features. Edge computing can be seen as an extension of the cloud in close physical proximity, in which some of the typical cloud computing loads are beneficial to run. This paper studies data analytics application development for integration of industrial IoT data and composition of application services executed on edge and cloud. A solution is designed to support heterogeneous hardware and run-time platforms, and focuses on the service layer that enables flexible orchestration of data flows and dynamic service compositions. The unified model and system architecture implemented, using the open Arrowhead Framework model, is verified through two representative industrial use cases.

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Section: Related Workmentioning

confidence: 99%

Dynamic Edge and Cloud Service Integration for Industrial IoT and Production Monitoring Applications of Industrial Cyber-Physical Systems

Hästbacka

Halme

Barna

et al. 2022

IEEE Trans. Ind. Inf.

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“…Workflow management is also supported through datadriven [10] concepts as well as separated workflow manager and executor entities [46]. Arrowhead allows to implement and maintain local cloud [41] specific systems as well, which are the custom applications, CPSs of the cloud.…”

Section: Arrowhead Frameworkmentioning

confidence: 99%

“…While addressing automated production issues through the Arrowhead framework, several alternatives came alive. These approaches defined the critical elements of the automated production, i.e., implemented following an SOA [44], using distributed systems and resources [46] and based on predefined but dynamically changing state machines [10], [47]. Considering the proposals, Section IV-B introduces the proposed new supporting core system, namely the Workflow Choreographer, which combines the mentioned concepts.…”

Section: A Requirements Of Digital Production In the Arrowheadmentioning

confidence: 99%

“…The Workflow Choreographer [10] is the main engine of automated production within the Arrowhead framework. From the production point of view, it processes the production order and accordingly implements the production recipe [46]. The industrial world has diverse scenarios; therefore, it is not possible to define a universal workflow manager.…”

Section: B Workflow Choreographer For Supporting Automated Production Within the Arrowheadmentioning

confidence: 99%

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Dynamic Multilevel Workflow Management Concept for Industrial IoT Systems

Kozma

Varga

Larrinaga

2021

IEEE Trans. Automat. Sci. Eng.

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Workflow management is implemented in manufacturing at many levels. The nature of processes varies at each level, hindering the use of a standard modeling or implementation solution. The creation of a flexible workflow management framework that overarches the heterogeneous business process levels is challenging. Still, one of the promises of the Industry 4.0 initiative is precisely this: to provide easy-to-use models and solutions that enable efficient execution of enterprise targets. By addressing this challenge, this article proposes a workflow execution model that integrates information and control flows of these levels while keeping their hierarchy. The overall model builds on the business process model and notation (BPMN) for modeling at the enterprise level and recipe modeling based on colored Petri net (CPN) at the production level. Models produced with both alternatives are implemented and executed in a framework supported by an enterprise service bus (ESB). Loosely coupled, late-bound system elements are connected through the Arrowhead framework, which is built upon the Service-Oriented Architecture (SOA) concept. To prove its feasibility, this article presents the practical application of the model via an automotive production scenario.Note to Practitioners-The methodology detailed in this article can serve as a basis for experts who are dealing with industrial workflows. Reacting to the requirements of Industry 4.0, i.e., the virtualization, decentralization, modularity, real-time capability, and service orientation, this article provides a concept that can answer all the defined criteria. First, it adopts a new two-level approach to workflow management, which makes the understanding and control of workflows easier, enhancing transparency. Furthermore, it demonstrates how-even completely different-applications and modeling languages can be integrated into a Service-Oriented Architecture (SOA). The presented com-Manuscript

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“…A decentralized approach is presented in the article [17]. ISA95 architecture is used with RAMI 4.0 on edge computing and SOA-based systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Scientific Workflow Makespan Minimization In Edge Multiple Service Providers Environment

Bukhari

Younus²,

Jaffari

et al. 2021

Preprint

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The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

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Workflow management for edge driven manufacturing systems

Cited by 18 publications

References 7 publications

Dynamic Edge and Cloud Service Integration for Industrial IoT and Production Monitoring Applications of Industrial Cyber-Physical Systems

Dynamic Edge and Cloud Service Integration for Industrial IoT and Production Monitoring Applications of Industrial Cyber-Physical Systems

Dynamic Multilevel Workflow Management Concept for Industrial IoT Systems

Scientific Workflow Makespan Minimization In Edge Multiple Service Providers Environment

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