Abstract
With the emergence of the Internet of Things (IoT), a new computing paradigm -Edge Computing- is evolving. Thanks to its horizontal scalability, this new paradigm leverages the rapid growth of devices and makes it in its favor. As a result, it improves scalability and reduces latency. However, simply adopting it does not necessarily guarantee meeting the Quality of Service (QoS), as many aspects need to be considered. To overcome this issue, there is a need for an intelligent Edge Computing. With machine learning abilities, the power of this paradigm can be extended to meet the IoT requirements. Motivated by this, in this paper, we present a tasks orchestration algorithm that is based on Fuzzy Decision Tree. It uses reinforcement learning that allows it to adapt to the unpredictable changes in the environment, and to provide better support for the heterogeneity of devices. The proposed algorithm has reduced the power consumption by 37% and failure rate by 57%, with a slightly shorter completion time compared to the existing solutions.
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References
Strategy Analytics. Strategy Analytics: Internet of Things Now Numbers 22 Billion Devices But Where Is The Revenue? (2019)
Mechalikh, C., Taktak, H., Moussa, F.: PureEdgeSim: a simulation toolkit for performance evaluation of cloud, fog, and pure edge computing environments. In: The 2019 International Conference on High Performance Computing & Simulation, pp. 700–707 (2019)
Santoro, D., Zozin, D., Pizzolli, D., De Pellegrini, F., Cretti, S.: Foggy: a platform for workload orchestration in a fog computing environment. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 231–234 (2017)
Yang, T., Zhang, H., Ji, H., Li, X.: Computation collaboration in ultra dense network integrated with mobile edge computing. In: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 1–5 (2017)
Sthapit, S., Hopgood, J.R., Thompson, J.: Distributed computational load balancing for real-time applications. In: 2017 25th European Signal Processing Conference (EUSIPCO), pp. 1385–1189 (2017)
Sonmez, C., Ozgovde, A., Ersoy, C.: Fuzzy workload orchestration for edge computing. IEEE Trans. Netw. Serv. Manag. 16(2), 769–782 (2019)
D’Angelo, M., Caporuscio, M.: Pure edge computing platform for the future internet. In: Federation of International Conferences on Software Technologies: Applications and Foundations, pp. 458–469 (2016)
Zhao, X., Zhao, L., Liang, K.: An energy consumption oriented offloading algorithm for fog computing. In: International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, pp. 293–301 (2016)
Wan, J., Chen, B., Wang, S., Xia, M., Li, D., Liu, C.: Fog computing for energy-aware load balancing and scheduling in smart factory. IEEE Trans. Ind. Inf. 14(10), 4548–4556 (2018)
Fan, W., Liu, Y.A., Tang, B., Wu, F., Wang, Z.: Computation offloading based on cooperations of mobile edge computing-enabled base stations. IEEE Access 6, 22622–22633 (2017)
Mechalikh, C., Taktak, H., Moussa, F.: Towards a scalable and QoS-aware load balancing platform for edge computing environments. In: The 2019 International Conference on High Performance Computing & Simulation, pp. 684–691 (2019)
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347–2376 (2015)
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Mechalikh, C., Taktak, H., Moussa, F. (2020). A Fuzzy Decision Tree Based Tasks Orchestration Algorithm for Edge Computing Environments. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_18
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DOI: https://doi.org/10.1007/978-3-030-44041-1_18
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