Orchestration of Microservices for IoT Using Docker and Edge Computing
Open accessAbstract
The world of connected devices has led to the rise of the Internet of Things paradigm, where applications rely on multiple devices, gathering and sharing data across highly heterogeneous networks. The variety of possible mechanisms, protocols, and hardware has become a hindrance in the development of architectures capable of addressing the most common IoT use cases, while abstracting services from the underlying communication subsystem. Moreover, the world is moving toward new strict requirements in terms of timeliness and low latency in combination with ultra-high availability and reliability. Thus, future IoT architectures will also have to support the requirements of these cyber-physical applications. In this regard, edge computing has been presented as one of the most promising solutions, relying on the cooperation of nodes by moving services directly to end devices and caching information locally. Therefore, in this article, we propose a modular and scalable architecture based on lightweight virtualization. The provided modularity, combined with the orchestration supplied by Docker, simplifies management and enables distributed deployments, creating a highly dynamic system. Moreover, characteristics such as fault tolerance and system availability are achieved by distributing the application logic across different layers, where failures of devices and micro-services can be masked by this natively redundant architecture, with minimal impact on the overall system performance. Experimental results have validated the implementation of the proposed architecture for on-demand services deployment across different architecture layers.
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Moving data processing and device control closer to the end-user reduces latency and dependency on external communications, maintaining the system functional even in case of interruptions of Internet access.
“An emerging trend in the IoT middleware context is to move data processing and device control closer to the enduser.This approach reduces latency and the dependency on external communications, maintaining the system functional even in the case of interruptions of Internet access.”
From Edge Computing and Microservices Middleware for Home Energy Management Systems (2022) · cites this paper as [3]
Cloud and fog computing approaches add delays to the communication with end devices, potentially affecting the end-user experience and increasing cost.
“However, these approaches add delays to the communication with enddevices [3], potentially affecting the end-user experience and increases the cost.”
From Edge Computing and Microservices Middleware for Home Energy Management Systems (2022) · cites this paper as [3]
Middleware platforms typically work under the concept of cloud computing, providing scalability, high processing capability, and nearly unlimited data storage capability.
“Typically, middleware platforms work under the concept of cloud computing, providing scalability, high processing capability, and nearly unlimited data storage capability [3].”
From Edge Computing and Microservices Middleware for Home Energy Management Systems (2022) · cites this paper as [3]
Moving data processing and device control closer to the end-user allows an additional security layer to be implemented between the edge and the cloud, leading to overall performance improvement.
“Moreover, this approach allows an additional security layer to be implemented between the edge and the cloud, leading to an overall performance improvement of the services offered [5] [3] [4].”
From Edge Computing and Microservices Middleware for Home Energy Management Systems (2022) · cites this paper as [3]
Cites
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244 · see citing works
An updated performance comparison of virtual machines and Linux containers
Felter, W., Ferreira, A., Rajamony, R., & Rubio, J. (2015) · IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass.2015.7095802 · Cited by 1,069 · see citing works
A survey on mobile edge computing
Ahmed, A., & Ahmed, E. (2016) · International Conference on Intelligent Systems and Control · doi:10.1109/isco.2016.7727082 · Cited by 611 · see citing works
Anderson, C. (2015) · IEEE Software · doi:10.1109/ms.2015.62 · Cited by 272 · see citing works
Evaluation of Docker as Edge computing platform
Ismail, B. I., Goortani, E. M., Karim, M. B. A., Tat, W. M., Setapa, S., Luke, J. Y., & Hoe, O. H. (2015) · IEEE Conference on Open Systems · doi:10.1109/icos.2015.7377291 · Cited by 259 · see citing works
Orchestration of containerized microservices for IIoT using Docker
Rufino, J., Alam, M., Ferreira, J., Rehman, A., & Tsang, K. F. (2017) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2017.7915594 · Cited by 84 · see citing works
Self-citationThe research and implementation of cloud computing platform based on docker
Liu, D., & Zhao, L. (2014) · doi:10.1109/iccwamtip.2014.7073453 · Cited by 76 · see citing works
Microservice-Based IoT for Smart Buildings
Khanda, K., Salikhov, D., Gusmanov, K., et al. (2017) · International Conference on Advanced Information Networking and Applications Workshops (WAINA) · doi:10.1109/waina.2017.77 · Cited by 73 · see citing works
Survey on low power real-time wireless MAC protocols
Bartolomeu, P., Alam, M., Ferreira, J., et al. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.09.004 · Cited by 44 · see citing works
Self-citationEnabling a lightweight Edge Gateway-as-a-Service for the Internet of Things
Morabito, R., Petrolo, R., Loscrì, V., et al. (2016) · International Conference on the Network of the Future (NOF) · doi:10.1109/nof.2016.7810110 · Cited by 35 · see citing works
Monitoring V2X applications using DevOps and docker
Rufino, J., Alam, M., & Ferreira, J. (2017) · doi:10.1109/isc2.2017.8090868 · Cited by 11 · see citing works
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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498 · see citing works
“hosting DL services, is in its infant [275], due to several critical challenges: 1) Handling DL deployment and management flexibly; 2) Achieving live migration of microservices to reduce”
BackgroundEdge Computing and Microservices Middleware for Home Energy Management Systems
Ferreira, L. C. B. C., Borchardt, A. D. R., Cardoso, G. D. S., Mendes Lemes, D. A., Sousa, G. R. D. R. D., Neto, F. B., de Lima, E. R., Fraidenraich, G., Cardieri, P., & Meloni, L. G. P. (2022) · IEEE Access · doi:10.1109/access.2022.3214229 · Cited by 28 · see citing works
“Typically, middleware platforms work under the concept of cloud computing, providing scalability, high processing capability, and nearly unlimited data storage capability [3].”
BackgroundInfluential
- Authors
- Alam, M., Rufino, J., Ferreira, J., Ahmed, S. H., Shah, N., & Chen, Y.
- Affiliations
- Xi’an Jiaotong-Liverpool University (CN)
- Instituto de Telecomunicações (PT)
- Georgia Southern University (US)
- COMSATS University Islamabad (PK)
- Hangzhou Dianzi University (CN)
- Venue
- IEEE Communications Magazine
- Published
- 2018-09
- Volume
- 56
- Issue
- 9
- Pages
- 118-123
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W2890344874
- Cited by
- 188
- References
- 11
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 22 · 2023: 30 · 2022: 41 · 2021: 37 · 2020: 25 · 2019: 18
- FWCI
- 12.39
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Communications Magazine (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by)
- Funders
- Fundação para a Ciência e a Tecnologia
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- Internet of Things, microservices, service orchestration, Docker, edge computing, lightweight virtualization, modular architecture, fault tolerance, distributed deployment
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