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The Implementation of a Cloud-Edge Computing Architecture Using OpenStack and Kubernetes for Air Quality Monitoring Application

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Abstract

The combination of edge and cloud computing is going to make the Internet of Things (IoT) rapid, light, and more reliable. IoT and cloud-edge computing are distinct disciples that have evolved separately over time. However, they are increasingly becoming interdependent, and are what the future holds. A crucial aspect is how to design a compound of cloud and edge computing architectures, and implement IoT effectively. In this paper, we proposed a combination of Cloud and Edge Computing architecture and built a set of an intelligent air-quality monitoring system in Tunghai University as a case study. In this case, we implemented container-based virtualization which constructs Kubernetes Minion (Nodes) in the Docker container service independently for each service on the Edge side. Finally, to monitor the high-performance computing systems, clusters, and networks, we used Ganglia Monitoring System. Ganglia collects relevant information such as Central Processing Unit (CPU), memory, network and usage of Protocol Data Unit (PDU) to monitor the power consumption and makes a measurement and evaluation for Kubernetes Pods.

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References

  1. Yang C-T, Liu J-C, Chen S-T, Hsin-Wen L (2017) Implementation of a big data accessing and processing platform for medical records in cloud. Journal of medical systems 41(10):149

    Article  Google Scholar 

  2. Varghese B, Buyya R (2018) Next generation cloud computing: new trends and research directions. Futur Gener Comput Syst 79:849–861

    Article  Google Scholar 

  3. Deng D-J, Lien S-Y, Lin C-C, Hung S-C, Chen W-B (2017) Latency control in software-defined mobile-edge vehicular networking. IEEE Commun Mag 55(8):87–93

    Article  Google Scholar 

  4. Lien S-Y, Deng D-J, Tsai H-L, Lin Y-P, Chen K-C (2017) Vehicular radio access to unlicensed spectrum. IEEE Wirel Commun 24(6):46–54

    Article  Google Scholar 

  5. Yuan X, Zhang M, Wang Q, Wang Y, Zuo J (2017) Evolution analysis of environmental standards: effectiveness on air pollutant emissions reduction. J Clean Prod 149:511–520

    Article  Google Scholar 

  6. Chao-Tung Yang, Shuo-Tsung Chen, Chih-Hung Chang, Walter Den, and Chia-Cheng Wu (2018). Implementation of an environmental quality and harmful gases monitoring system in cloud. Journal of Medical and Biological Engineering, pages 1–14

  7. Kozhirbayev Z, Sinnott RO (2017) A performance comparison of container-based technologies for the cloud. Future Generation Computer Systems 68:175–182

    Article  Google Scholar 

  8. Yang C-T, Chen C-J, Chen T-Y (2017) Implementation of ceph storage with big data for performance comparison. Lecture Notes in Electrical Engineering 424:625–633

    Article  Google Scholar 

  9. A Mikula, D Adamová, M Adam, J Chudoba, and J Švec (2016). Grid site monitoring and log processing using elk. CEUR Workshop Proceedings

  10. Endah Kristiani, Chao-Tung Yang, Yuan Ting Wang, and Chin-Yin Huang (2018). Implementation of an edge computing architecture using openstack and kubernetes. In International Conference on Information Science and Applications, pages 675–685. Springer

  11. Peng Shu, Rong Gu, Qianhao Dong, Chunfeng Yuan, and Yihua Huang (2016). Accelerating big data applications on tiered storage system with various eviction policies. In 2016 IEEE Trustcom/BigDataSE/ISPA, pages 1350–1357. IEEE

  12. Salman Taherizadeh, Andrew C Jones, Ian Taylor, Zhiming Zhao, and Vlado Stankovski (2017). Monitoring self-adaptive applications within edge computing frameworks: A state-of-the-art review. Journal of Systems and Software

  13. Sharma V, Song F, You I, Atiquzzaman M (2017) Energy efficient device discovery for reliable communication in 5g-based iot and bsns using unmanned aerial vehicles. J Netw Comput Appl 97:79–95

    Article  Google Scholar 

  14. Jan Medved, Robert Varga, Anton Tkacik, and Ken Gray (2014). Opendaylight: towards a model-driven sdn controller architecture. In World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2014 IEEE 15th International Symposium on a, pages 1–6. IEEE

  15. Rabindra K Barik, Rakesh K Lenka, K Rahul Rao, and Devam Ghose (2016). Performance analysis of virtual machines and containers in cloud computing. In Computing, Communication and Automation (ICCCA), 2016 International Conference on, pages 1204–1210. IEEE

  16. Deng D-J, Lin Y-P, Yang X, Zhu J, Li Y-B, Luo J, Chen K-C (2017) Ieee 802.11 ax: highly efficient wlans for intelligent information infrastructure. IEEE Communications Magazine 55(12):52–59

    Article  Google Scholar 

  17. Kochovski P, Stankovski V (2018) Supporting smart construction with dependable edge computing infrastructures and applications. Automation in Construction 85:182–192

    Article  Google Scholar 

  18. Yuan Ai, Mugen Peng, and Kecheng Zhang (2017). Edge computing technologies for internet of things: a primer. Digital Communications and Networks

  19. Openstack. https://www.openstack.org/, 2017

  20. Yoji Yamato (2016). Proposal of optimum application deployment technology for heterogeneous iaas cloud. arXiv preprint arXiv:1611.09570

  21. Yamato Y (2017) Optimum application deployment technology for heterogeneous iaas cloud. Journal of Information Processing 25:56–58

    Article  Google Scholar 

  22. Netto HV, Lung LC, Correia M, Luiz AF, de Souza LMS (2017) State machine replication in containers managed by kubernetes. Journal of Systems Architecture 73:53–59

    Article  Google Scholar 

  23. Kubernetes. https://kubernetes.io/, 2017

  24. Mqtt. http://mqtt.org/, 2017

  25. Ejaz Ahmed and Mubashir Husain Rehmani (2017). Mobile edge computing: opportunities, solutions, and challenges

  26. Pei-Hsuan Tsai, Hua-Jun Hong, An-Chieh Cheng, and Cheng-Hsin Hsu (2017). Distributed analytics in fog computing platforms using tensorflow and kubernetes. In Network Operations and Management Symposium (APNOMS), 2017 19th Asia-Pacific, pages 145–150. IEEE

  27. Malik A, Ahmed J, Qadir J, Ilyas MU (2017) A measurement study of open source sdn layers in openstack under network perturbation. Computer Communications 102:139–149

    Article  Google Scholar 

  28. Satria D, Park D, Jo M (2017) Recovery for overloaded mobile edge computing. Futur Gener Comput Syst 70:138–147

    Article  Google Scholar 

  29. Ke Zhan and Ai Hua Piao (2016). Optimization of ceph reads/writes based on multi-threaded algorithms. In 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pages 719–725. IEEE

  30. Poat MD, Lauret J (2016) Performance and advanced data placement techniques with ceph’s distributed storage system. Journal of Physics: Conference Series 762(1)

  31. Dipti Shankar, Xiaoyi Lu, and Dhabaleswar K DK Panda (2016). Boldio: A hybrid and resilient burst-buffer over lustre for accelerating big data i/o. In 2016 IEEE International Conference on Big Data (Big Data), pages 404–409. IEEE

  32. Y Huang, Y Yesha, M Halem, Y Yesha, and S Zhou (2016). Yinmem: A distributed parallel indexed in-memory computation system for large scale data analytics. In 2016 IEEE International Conference on Big Data (Big Data), pages 214–222

  33. Mavridis I, Karatza H (2017) Performance evaluation of cloud-based log file analysis with apache hadoop and apache spark. J Syst Softw 125:133–151

    Article  Google Scholar 

  34. Alexey Svyatkovskiy, Kosuke Imai, Mary Kroeger, and Yuki Shiraito (2016). Large-scale text processing pipeline with apache spark. In 2016 IEEE International Conference on Big Data (Big Data), pages 3928–3935. IEEE

  35. Pingkan PI Langi, Warsun Najib, Teguh Bharata Aji, et al. (2015) An evaluation of twitter river and logstash performances as elasticsearch inputs for social media analysis of twitter. In 2015 International Conference on Information & Communication Technology and Systems (ICTS), pages 181–186. IEEE

  36. Kumari S, Khan MK, Atiquzzaman M (2015) User authentication schemes for wireless sensor networks: A review. Ad Hoc Networks 27:159–194

    Article  Google Scholar 

  37. Pacevic R, Kaceniauskas A (2017) The development of vislt visualization service in openstack cloud infrastructure. Adv Eng Softw 103:46–56

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Ministry of Science and Technology, Taiwan (R.O.C.), under grants number 107-2221-E-029-008 and 107-2218-E-029-003.

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Authors and Affiliations

  1. Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, 40704, Taiwan, R.O.C.

    Endah Kristiani & Chin-Yin Huang

  2. Department of Informatics, Krida Wacana Christian University, Jakarta, 11470, Indonesia

    Endah Kristiani

  3. Department of Computer Science, Tunghai University, Taichung, 40704, Taiwan, R.O.C.

    Chao-Tung Yang & Po-Cheng Ko

  4. Cloud Computing Laboratory, Chunghwa Telecom Laboratories, Taoyuan City, 326, Taiwan, R.O.C.

    Yuan-Ting Wang

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  1. Endah Kristiani
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  2. Chao-Tung Yang
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  5. Po-Cheng Ko
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Correspondence to Chao-Tung Yang.

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Kristiani, E., Yang, CT., Huang, CY. et al. The Implementation of a Cloud-Edge Computing Architecture Using OpenStack and Kubernetes for Air Quality Monitoring Application. Mobile Netw Appl 26, 1070–1092 (2021). https://doi.org/10.1007/s11036-020-01620-5

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