Abstract
Cloud computing benefits emerging Inter of Things (IoT) applications by providing virtualized computing platform in the cloud. However, increasing demands of low-latency services motivates the placement of computing platform on the edge of network, a new computing paradigm named fog computing. This study assumes container as virtualized computing platform and uses Kubernetes to manage and control geographically distributed containers. We consider the design and implementation of an auto-scaling scheme in this environment, which dynamically adjusts the number of application instances to strike a balance between resource usage and application performance. The key components of the implementation include a scheme to monitor load status of physical hosts, an algorithm that determines the appropriate number of application instances, and an interface to Kubernetes to perform the adjustment. Experiments have been conducted to investigate the performance of the proposed scheme. The results confirm the effectiveness of the proposed scheme in reducing application response time.
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References
de la Bastida, D., Lin, F.J.: OpenStack-based highly scalable IoT/M2M platforms. In: IEEE International Conference on Internet of Things, Exeter, UK, June 2017
Bellavista, P., Zanni, A.: Feasibility of fog computing deployment based on Docker containerization over RaspberryPi. In: Proceedings of the 18th International Conference on Distributed Computing and Networking, January 2017
Brogi, A., Mencagli, G., Neri, D., Soldani, J., Torquati, M.: Container-based support for autonomic data stream processing through the fog. In: Heras, D.B., Bougé, L. (eds.) Euro-Par 2017. LNCS, vol. 10659, pp. 17–28. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75178-8_2
Chang, C.C., Yang, S.R., Yeh, E.H., Lin, P., Jeng, J.Y.: A kubernetes-based monitoring platform for dynamic cloud resource provisioning. In: IEEE Global Communications Conference, December 2017
Dupont, C., Giaffreda, R., Capra, L.: Edge computing in IoT context: horizontal and vertical Linux container migration. In: Global Internet of Things Summit, June 2017
Hao, Z., Novak, E., Yi, S.: Challenges and software architecture for fog computing. IEEE Internet Comput. 21(2), 44–53 (2017)
Hu, P., Ning, H., Qiu, T., Zhang, Y., Luo, X.: Fog computing based face identification and resolution scheme in internet of things. IEEE Trans. Ind. Inform. 13(4), 1910–1920 (2017)
Ismail, B.I., Goortani, E.M., Karim, M.B.A.: Evaluation of Docker as edge computing platform. In: 2015 IEEE Conference on Open Systems, Bandar Melaka, Malaysia, August 2015
kubernetes: production-grade container orchestration. https://kubernetes.io/
Mebrek, A., Merghem-Boulahia, L., Esseghir, M.: Efficient green solution for a balanced energy consumption and delay in the IoT-fog-cloud computing. In: 16th International Symposium on Network Computing and Applications, Cambridge, MA, USA, October 2017
Rocket: a security-minded, standards-based container engine. https://coreos.com/rkt
Tsai, P.H., Hong, H.J., Cheng, A.C.: Distributed analytics in fog computing platforms using TensorFlow and Kubernetes. In: 19th Asia-Pacific Network Operations and Management Symposium, Seoul, South Korea, September 2017
Yu, T., Wang, X., Shami, A.: A novel fog computing enabled temporal data reduction scheme in IoT systems. In: IEEE Global Communications Conference, December 2017
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Zheng, WS., Yen, LH. (2019). Auto-scaling in Kubernetes-Based Fog Computing Platform. In: Chang, CY., Lin, CC., Lin, HH. (eds) New Trends in Computer Technologies and Applications. ICS 2018. Communications in Computer and Information Science, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-13-9190-3_35
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DOI: https://doi.org/10.1007/978-981-13-9190-3_35
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