Edge-Based Microservices Architecture for Internet of Things: Mobility Analysis Case Study

Leppänen, Teemu; Savaglio, Claudio; Lovén, Lauri; Tommi, Jarvenpaa; Ehsani, Rouhollah; Peltonen, Ella; Fortino, Giancarlo; Riekki, Jukka

doi:10.1109/globecom38437.2019.9014273

Cited by 15 publications

(10 citation statements)

References 17 publications

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“…Thus, several database systems can be used in a microservice architecture, like Cassandra [57], InfluxDB [136,147], MySQL [55,78], MongoDB [18,29]. Moreover, the microservices are developed using different programming languages and frameworks such as, Python [55,78,79,146,136,81,139,54,140,142,121,28], Java OSGi [110,125], SpringBoot [71,146,115], Java [41,29,79,128,53,94], Golang [18,146,84], Node.JS [125,137], C++ [78,79,140], R [17], and even pure C [126].…”

Section: Conclusion Validitymentioning

confidence: 99%

“…Further, the API Gateway is another example of an architectural feature employed in many solutions. As far as the approaches, the architectures may use a single [16,17], two [18,19], or even all three [13,20] tiers (Edge, Fog, Cloud) in the architecture to host and run the microservices to achieve the expected QoS [13,20]. Other authors proposed extra tiers beyond the traditional 3-tier approach.…”

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confidence: 99%

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Relating Edge Computing and Microservices by means of Architecture Approaches and Features, Orchestration, Choreography, and Offloading: A Systematic Literature Review

Castro¹,

Rigo²

2023

Preprint

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Context: Microservices running and being powered by Edge Computing have been gaining much attention in the industry and academia. Since 2014, when Martin Fowler popularized the Microservice term, many studies have been published relating these subjects to explore how the Edge's low-latency feature could be combined with the high throughput of the distributed paradigm from Microservices. Objective: Identifying how Microservices work together with Edge Computing whereas they take advantage when running on Edge. Method: In order to better understand this relationship, we first identified its key concepts, which are: architecture approaches and features, microservice composition (orchestration/choreography), and offloading. Afterward, we conducted a Systematic Literature Review (SLR) as the survey method. Results: We reviewed 111 selected studies and built a taxonomy of Microservices on Edge Computing demonstrating their current architecture approaches and features, composition, and offloading modes. Moreover, we identify the research gaps and trends. Conclusion: This paper is a step forward to help researchers and professionals get a general overview of how Microservices and Edge have been related in the last years. It also discusses gaps and research trends. This SLR will also be a good introduction for new researchers in Edge and Microservices.

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Section: Conclusion Validitymentioning

confidence: 99%

mentioning

confidence: 99%

Relating Edge Computing and Microservices by means of Architecture Approaches and Features, Orchestration, Choreography, and Offloading: A Systematic Literature Review

Castro¹,

Rigo²

2023

Preprint

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show abstract

“…It brings a number of advantages in terms of flexibility and scalability. A few studies have been performed in the context of deployment of the MSA for various IoT applications based on the edge [17], [18] and mist computing [19]. Microservices are widely deployed in cloud data centers or edge cluster nodes in the last few years.…”

Section: B Monolithic Vs Microservices Vs Nanoservicesmentioning

confidence: 99%

Resource-Aware Dynamic Service Deployment for Local IoT Edge Computing: Healthcare Use Case

et al. 2021

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Edge computing is a novel computing paradigm moving server resources closer to enddevices. In the context of IoT, Edge computing is a centric technology for enabling reliable, context-aware and low-latency services for several application areas such as smart healthcare, smart industry and smart cities. In our previous work, we have proposed a three-tier IoT Edge architecture and a virtual decentralized service platform based on lightweight microservices, called nanoservices, running on it. Together, these proposals form a basis for virtualizing the available local computational capacity and utilizing it to provide localized resource-efficient IoT services based on the applications' need. Furthermore, locally deployed functions are resilient to access network problems and can limit the propagation of sensitive user data for improved privacy. In this paper, we propose an automatic service and resource discovery mechanism for efficient on-the-fly deployment of nanoservices on local IoT nodes. As use case, we have selected a healthcare remote monitoring scenario, which requires high service reliability and availability in a highly dynamic environment. Based on the selected use case, we propose a real-world prototype implementation of the proposed mechanism on Raspberry Pi platform. We evaluate the performance and resourceefficiency of the proposed resource matching function with two alternative deployment approaches: containerized and non-containerized deployment. The results show that the containerized deployment is more resource-efficient, while the resource discovery and matching process takes approximately 6-17 seconds, where containerization adds only 1-1.5 seconds. This can be considered a feasible price for streamlined service management, scalability, resource-efficiency and fault-tolerance. IoT, edge computing, distributed computing, virtualization, resource discovery, microservices, nanoservices. INDEX TERMS

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“…Three criteria, namely round trip time, CPU time delay, and time distribution were considered to assess the system performance of containerization. In [31], the authors looked into the latency performance of multi-access edge computing platform running a containerbased user mobility analysis service and concluded that the low latency promise in the edge could be guaranteed with a containerized implementation. The authors in [32] provided a thorough performance benchmarking of stream data latency, scalability and resource utilization on a container-based edge This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Containerization in Edge-Cloud Computing Stacks for Industrial Applications: A Client Perspective

Liu

Lan

Pang

et al. 2021

IEEE Open J. Ind. Electron. Soc.

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Today, the edge-cloud computing paradigm starts to gain increasing popularity, aiming to enable short latency, fast decision-making and intelligence at the network edge, especially for industrial applications. The container-based virtualization technology has been put on the roadmap by the industry to implement edge-cloud computing infrastructures. Has the performance of the container-based edge-cloud computing stacks reached industry requirement? In this paper, from the industrial client perspective, we provide a performance evaluation methodology and apply it to the state-of-the-art containerization-based edge-cloud computing infrastructures. The influences of the message sending interval, payload, network bandwidth and concurrent devices on full stack latency are measured, and the processing capability of executing machine learning tasks are benchmarked. The results show that containerization on the edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the edge-cloud computing paradigm. However, there is a large room for performance improvement between current implementation of the edge-cloud infrastructure and the demanding requirements anticipated by time-critical industrial applications. We also emphasize and showcase that partitioning of an industrial application into microservices throughout the whole stack can be considered during solution design. The proposed evaluation methodology can be a reference to users of edge-cloud computing as well as developers to get a client perspective overview of system performance.

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Edge-Based Microservices Architecture for Internet of Things: Mobility Analysis Case Study

Cited by 15 publications

References 17 publications

Relating Edge Computing and Microservices by means of Architecture Approaches and Features, Orchestration, Choreography, and Offloading: A Systematic Literature Review

Relating Edge Computing and Microservices by means of Architecture Approaches and Features, Orchestration, Choreography, and Offloading: A Systematic Literature Review

Resource-Aware Dynamic Service Deployment for Local IoT Edge Computing: Healthcare Use Case

Performance Evaluation of Containerization in Edge-Cloud Computing Stacks for Industrial Applications: A Client Perspective

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