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Machine Learning for Fog Computing: Perceiving QoE for IoT Applications

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Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023 (AISI 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 184))

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Abstract

Since the Internet of Things (IoT) is a rapidly growing industry, so does the range of IoT applications; IoT transforms every aspect of our lives, from smart homes and cities to healthcare and agriculture. With the increasing popularity of IoT applications in many different areas, the round-trip delay of data processing in the cloud affects the user’s quality perception. A portable operating system distribution, high environmental consistency, and resource isolation are all features of the lightweight application virtualization technology known as containerization. Mainstream cloud service providers for automated application administration have widely incorporated container technologies into distributed system architectures. However, the distributed nature of cloud and edge computing environments and workloads significantly increases the complexity of orchestration systems. Therefore, the Quality of Experience (QoE) is required for Internet of Things (IoT) applications. Machine Learning (ML) algorithms are employed by container orchestration systems for modeling, predicting the multi-dimensional performance metrics, and improving the overall QoE. The main objective of this paper is to provide a comprehensive review of existing machine learning enablement of container orchestration. Moreover, we discuss and classify the ML implementation challenges in IoT applications and enlist the applications of machine learning algorithms with IoT.

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Correspondence to Eman Ahmed .

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Ahmed, E., Nashaat, H., Rizk, R. (2023). Machine Learning for Fog Computing: Perceiving QoE for IoT Applications. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_42

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