EdgeFlow—Developing and Deploying Latency-Sensitive IoT Edge Applications

Avasalcai, Cosmin; Zarrin, Bahram; Dustdar, Schahram

doi:10.1109/jiot.2021.3101449

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…The goal of resource management and configuration techniques in the FORA FCP is to provide the necessary computation and communication resources to all applications, balance the overall resource utilization landscape, and provide realtime guarantees for critical applications [15]. To this end, the FORA FCP uses application deployment techniques [26] for submitting applications to the FCP and application migration methods [27] for migrating application between FNs. The FORA FCP provides cross-layer resource allocation mechanism [28], so that resources all over the FCP can be exploited if necessary and based on the demands of these applications, especially in the cases where the FCP is dynamic, i.e, FNs may enter and leave the FCP.…”

Section: Resource Management Orchestration and Configurationmentioning

confidence: 99%

The FORA European Training Network on Fog Computing for Robotics and Industrial Automation

Barzegaran

Pop

2023

2023 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Fog Computing for Robotics and Industrial Automation, FORA, was a European Training Network which focused on future industrial automation architectures and applications based on an emerging technology, called Fog Computing. The research project focused on research related to Fog Computing with applicability to industrial automation and manufacturing.The main outcome of the FORA project was the development of a deterministic Fog Computing Platform (FCP) to be used for implementing industrial automation and robotics solutions for Industry 4.0. This paper reports on the scientific outcomes of the FORA project. FORA has proposed a reference system architecture for Fog Computing, which was published as an open Architecture Analysis Design Language (AADL) model. The technologies developed in FORA include fog nodes and hypervisors, resource management mechanisms and middleware for deploying scalable Fog Computing applications, while guaranteeing the non-functional properties of the virtualized industrial control applications, and methods and processes for assuring the safety and security of the FCP. Several industrial use cases were used to evaluate the suitability of the FORA FCP for the Industrial IoT area, and to demonstrate how the platform can be used to develop industrial control applications and data analytics applications.

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Section: Resource Management Orchestration and Configurationmentioning

confidence: 99%

The FORA European Training Network on Fog Computing for Robotics and Industrial Automation

Barzegaran

Pop

2023

2023 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

View full text Add to dashboard Cite

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“…The opportunistic usage of edge cloud resources to improve latency and jitter has been extensively discussed in [25], [26] and also represents one of the key factors pushing for wide adoption of this computing model [27].…”

Section: Related Workmentioning

confidence: 99%

TEMPOS: QoS Management Middleware for Edge Cloud Computing FaaS in the Internet of Things

et al. 2022

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Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, call for Quality of Service (QoS) management to guarantee/control performance indicators, even in presence of many sources of ''stochastic noise'' in real deployment environments, from scarcely available bandwidth in a time window to concurrent usage of virtualized processing resources. This paper proposes a novel IoT-oriented middleware that i) considers and coordinates together different aspects of QoS monitoring, control, and management for different kinds of virtualized resources (from networking to processing) in a holistic way, and ii) specifically targets deployment environments where edge cloud resources are employed to enable the Serverless paradigm in the cloud continuum. The reported experimental results show how it is possible to achieve the desired QoS differentiation by coordinating heterogeneous mechanisms and technologies already available in the market. This demonstrates the feasibility of effective QoS-aware management of virtualized resources in the cloud-to-things continuum when considering a Serverless provisioning scenario, which is completely original in the related literature to the best of our knowledge.INDEX TERMS Edge cloud computing, FaaS, Internet of Things, interoperability, middleware, QoS management, serverless.

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“…great significance to accelerate the development of intelligent applications in industry, such as industrial surveillance, smart manufacturing, and predictive maintenance. However, due to the heterogeneity and limited computing and communication capabilities of edge nodes, the rapid increase of real-time IIoT applications makes edge intelligence face a great challenge in terms of fast response, low latency, and efficient bandwidth utilization [2]. Therefore, how to satisfy rigorous requirements of IIoT systems is a significant problem to be solved.…”

mentioning

confidence: 99%

An Intelligent Deterministic Scheduling Method for Ultralow Latency Communication in Edge Enabled Industrial Internet of Things

Yang

et al. 2023

IEEE Trans. Ind. Inf.

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Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, Time Sensitive Network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then a non-collision theory based deterministic scheduling (NDS) method is proposed to achieve ultra-low latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultra-low latency services and guarantee efficient bandwidth utilization.

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EdgeFlow—Developing and Deploying Latency-Sensitive IoT Edge Applications

Cited by 12 publications

References 34 publications

The FORA European Training Network on Fog Computing for Robotics and Industrial Automation

The FORA European Training Network on Fog Computing for Robotics and Industrial Automation

TEMPOS: QoS Management Middleware for Edge Cloud Computing FaaS in the Internet of Things

An Intelligent Deterministic Scheduling Method for Ultralow Latency Communication in Edge Enabled Industrial Internet of Things

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