Orchestration of Microservices for IoT Using Docker and Edge Computing

Alam, Muhammad; Rufino, João; Ferreira, Joaquim; Ahmed, Syed Hassan; Shah, Nadir; Chen, Yuanfang

doi:10.1109/mcom.2018.1701233

Cited by 165 publications

(90 citation statements)

References 6 publications

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“…Authors of [50] provide a highly dynamic system, that is, a modular based on a scalable architecture that relies on lightweight virtualization techniques. The proposed architecture consists of three main layers: sensing (edge devices), mediation (gateways), and enterprise layer (cloud).…”

Section: Data/video Analysis In the Edgementioning

confidence: 99%

5G support for Industrial IoT Applications— Challenges, Solutions, and Research gaps

Varga

Pető

Frankó

et al. 2020

Sensors

201

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Industrial IoT has special communication requirements, including high reliability, low latency, flexibility, and security. These are instinctively provided by the 5G mobile technology, making it a successful candidate for supporting Industrial IoT (IIoT) scenarios. The aim of this paper is to identify current research challenges and solutions in relation to 5G-enabled Industrial IoT, based on the initial requirements and promises of both domains. The methodology of the paper follows the steps of surveying state-of-the art, comparing results to identify further challenges, and drawing conclusions as lessons learned for each research domain. These areas include IIoT applications and their requirements; mobile edge cloud; back-end performance tuning; network function virtualization; and security, blockchains for IIoT, Artificial Intelligence support for 5G, and private campus networks. Beside surveying the current challenges and solutions, the paper aims to provide meaningful comparisons for each of these areas (in relation to 5G-enabled IIoT) to draw conclusions on current research gaps.

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Section: Data/video Analysis In the Edgementioning

confidence: 99%

5G support for Industrial IoT Applications— Challenges, Solutions, and Research gaps

Varga

Pető

Frankó

et al. 2020

Sensors

201

View full text Add to dashboard Cite

show abstract

“…This framework utilizes Docker containers, combined with pub/sub messaging, to achieve low latency analytics. Alam et al [35] propose a microservices-based architecture by employing Docker virtualization and edge computing. This approach, combined with application management, offers fault tolerance and distributes application logic across cloud, fog, and edge devices.…”

Section: Iot Architectures and Edge-cloud Distributed Frameworkmentioning

confidence: 99%

IoTEF: A Federated Edge-Cloud Architecture for Fault-Tolerant IoT Applications

et al. 2020

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The evolution of Internet of Things (IoT) technology has led to an increased emphasis on edge computing for Cyber-Physical Systems (CPS), in which applications rely on processing data closer to the data sources, and sharing the results across heterogeneous clusters. This has simplified the data exchanges between IoT/CPS systems, the cloud, and the edge for managing low latency, minimal bandwidth, and fault-tolerant applications. Nonetheless, many of these applications administer data collection on the edge and offer data analytic and storage

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“…Somewhat similarly, Alam et al define for each of the three fog layers (end device, fog node, cloud) a set of components [3]. The components in end devices are: collaborative and autonomous decision-making, micro analytics, self-healing, sensing input, mesh computing, display output, and local caching.…”

Section: Architecture In Fog Computingmentioning

confidence: 99%

“…An architecture view can also combine two architecture dimensions. For example, Alam et al combine the device and functionality dimensions by defining for each of the three device layers (end device, fog node, cloud) a set of functional building blocks [3]. Figure 3b shows schematically how such a view fits into the proposed framework.…”

Section: Architecture Viewsmentioning

confidence: 99%

Notions of architecture in fog computing

Mann

2020

Computing

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Fog computing is becoming a popular paradigm for bringing the advantages of the cloud nearer to the network edge. This way, computational tasks can be offloaded from end devices to nearby fog nodes, thus benefiting from high computational power and low latency at the same time. Architecture plays a central role in fog computing. Many papers on fog computing address architectural questions. However, a closer look reveals that different papers use the term “architecture” for very different concepts. This is rooted in the multi-disciplinary nature of the fog computing paradigm. The different communities involved in fog computing—network, hardware, system software, application software—all use the term “architecture,” but with different meaning. To facilitate the mutual understanding of architectural issues in fog computing, this paper introduces a conceptual framework for reasoning about architecture in fog computing. This conceptual framework uses three independent dimensions to describe architecture. Based on the three architecture dimensions, several architecture views can be defined to serve the different viewpoints of the involved disciplines, and to highlight different aspects of the architecture. The conceptual framework is validated using a literature mapping study.

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Orchestration of Microservices for IoT Using Docker and Edge Computing

Cited by 165 publications

References 6 publications

5G support for Industrial IoT Applications— Challenges, Solutions, and Research gaps

5G support for Industrial IoT Applications— Challenges, Solutions, and Research gaps

IoTEF: A Federated Edge-Cloud Architecture for Fault-Tolerant IoT Applications

Notions of architecture in fog computing

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