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Clustered Single-Board Devices with Docker Container Big Stream Processing Architecture

N. Penchalaiah1, Abeer S. Al-Humaimeedy2, Mashael Maashi3, J. Chinna Babu4,*, Osamah Ibrahim Khalaf5, Theyazn H. H. Aldhyani6

1 Department of CSE, AITS, Rajampet, 516126, Andhra Pradesh, India
2 Software Engineering Department, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh, 11616, Saudi Arabia
3 Software Engineering Department, King Saud University, Riyadh, 11543, Saudi Arabia
4 Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, 516126, Andhra Pradesh, India
5 Al-Nahrain University, Al-Nahrain Nanorenewable Energy Research Center, Baghdad, 10072, Iraq
6 Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa, 31982, Saudi Arabia

* Corresponding Author: J. Chinna Babu. Email: email

Computers, Materials & Continua 2022, 73(3), 5349-5365.


The expanding amounts of information created by Internet of Things (IoT) devices places a strain on cloud computing, which is often used for data analysis and storage. This paper investigates a different approach based on edge cloud applications, which involves data filtering and processing before being delivered to a backup cloud environment. This Paper suggest designing and implementing a low cost, low power cluster of Single Board Computers (SBC) for this purpose, reducing the amount of data that must be transmitted elsewhere, using Big Data ideas and technology. An Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and Docker. To obtain system data and analyze the setup’s performance a Prometheus-based stack monitoring and alerting solution in the cloud based market is employed. This Paper assesses the system’s complexity and demonstrates how containerization can improve fault tolerance and maintenance ease, allowing the suggested solution to be used in industry. An evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture, taking into consideration the suggested solution’s resource use in respect to device restrictions.


Cite This Article

APA Style
Penchalaiah, N., Al-Humaimeedy, A.S., Maashi, M., Babu, J.C., Khalaf, O.I. et al. (2022). Clustered single-board devices with docker container big stream processing architecture. Computers, Materials & Continua, 73(3), 5349-5365.
Vancouver Style
Penchalaiah N, Al-Humaimeedy AS, Maashi M, Babu JC, Khalaf OI, Aldhyani THH. Clustered single-board devices with docker container big stream processing architecture. Comput Mater Contin. 2022;73(3):5349-5365
IEEE Style
N. Penchalaiah, A.S. Al-Humaimeedy, M. Maashi, J.C. Babu, O.I. Khalaf, and T.H.H. Aldhyani "Clustered Single-Board Devices with Docker Container Big Stream Processing Architecture," Comput. Mater. Contin., vol. 73, no. 3, pp. 5349-5365. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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