SLedge: Scheduling and Load Balancing for a Stream Processing EDGE Architecture

Hidalgo, Nicolás; Rosas, Erika; Saavedra, Teodoro; Morales, Jefferson

doi:10.3390/app12136474

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…In [ 62 ], the latency of real devices, as well as the results of a simulated video processing, are recorded and subsequently imported in the network simulator NS-3. In this manner, more realism regarding the input data is achieved.…”

Section: State Of the Artmentioning

confidence: 99%

Virtual Commissioning of Distributed Systems in the Industrial Internet of Things

Rosenberger

Selig

Ristić

et al. 2023

Sensors

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With the convergence of information technology (IT) and operational technology (OT) in Industry 4.0, edge computing is increasingly relevant in the context of the Industrial Internet of Things (IIoT). While the use of simulation is already the state of the art in almost every engineering discipline, e.g., dynamic systems, plant engineering, and logistics, it is less common for edge computing. This work discusses different use cases concerning edge computing in IIoT that can profit from the use of OT simulation methods. In addition to enabling machine learning, the focus of this work is on the virtual commissioning of data stream processing systems. To evaluate the proposed approach, an exemplary application of the middleware layer, i.e., a multi-agent reinforcement learning system for intelligent edge resource allocation, is combined with a physical simulation model of an industrial plant. It confirms the feasibility of the proposed use of simulation for virtual commissioning of an industrial edge computing system using Hardware-in-the-Loop. In summary, edge computing in IIoT is highlighted as a new application area for existing simulation methods from the OT perspective. The benefits in IIoT are exemplified by various use cases for the logic or middleware layer using physical simulation of the target environment. The relevance for real-life IIoT systems is confirmed by an experimental evaluation, and limitations are pointed out.

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Section: State Of the Artmentioning

confidence: 99%

Virtual Commissioning of Distributed Systems in the Industrial Internet of Things

Rosenberger

Selig

Ristić

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…After initial processing, the Data Points are aggregated in a Cloud Layer through MQ Telemetry Transport (MQTT) [31]. SLedge [32] is an edge Stream Processing architecture, centered on Apache Storm [17], optimized for disaster response scenarios. A model for efficiently distributing tasks over fog computing infrastructure, edge nodes, and mobile devices was provided.…”

Section: Related Workmentioning

confidence: 99%

“…There were no considerations regarding resource usage or network connectivity. For SLedge [32], the evaluation did not find the upper processing limits, but compared the processing latency for real use cases. In [10], the reliability and scalability of the proposed architecture were evaluated, but the evaluation considered at least four nodes to distribute the work.…”

Section: Related Workmentioning

confidence: 99%

IRONEDGE: Stream Processing Architecture for Edge Applications

et al. 2023

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This paper presents IRONEDGE, an architectural framework that can be used in different edge Stream Processing solutions for “Smart Infrastructure” scenarios, on a case-by-case basis. The architectural framework identifies the common components that any such solution should implement and a generic processing pipeline. In particular, the framework is considered in the context of a study case regarding Internet of Things (IoT) devices to be attached to rolling stock in a railway. A lack of computation and storage resources available in edge devices and infrequent network connectivity are not often seen in the existing literature, but were considered in this paper. Two distinct implementations of IRONEDGE were considered and tested. One, identified as Apache Kafka with Kafka Connect (K0-WC), uses Kafka Connect to pass messages from MQ Telemetry Transport (MQTT) to Apache Kafka. The second scenario, identified as Apache Kafka with No Kafka Connect (K1-NC), allows Apache Storm to consume messages directly. When the data rate increased, K0-WC showed low throughput resulting from high losses, whereas K1-NC displayed an increase in throughput, but did not match the input rate for the Data Reports. The results showed that the framework can be used for defining new solutions for edge Stream Processing scenarios and identified a reference implementation for the considered study case. In future work, the authors propose to extend the evaluation of the architectural variation of K1-NC.

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SLedge: Scheduling and Load Balancing for a Stream Processing EDGE Architecture

Cited by 2 publications

References 29 publications

Virtual Commissioning of Distributed Systems in the Industrial Internet of Things

Virtual Commissioning of Distributed Systems in the Industrial Internet of Things

IRONEDGE: Stream Processing Architecture for Edge Applications

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