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Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence

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Abstract

Edge intelligence has become popular recently since it brings smartness and copes with some shortcomings of conventional technologies such as cloud computing, Internet of Things (IoT), and centralized AI adoptions. However, although utilizing edge intelligence contributes to providing smart systems such as automated driving systems, smart cities, and connected healthcare systems, it is not free from limitations. There exist various challenges in integrating AI and edge computing, one of which is addressed in this paper. Our main focus is to handle the adoption of AI methods on resource-constrained edge devices. In this regard, we introduce the concept of Edge devices as a Service (EdaaS) and propose a quality of service (QoS) and quality of experience (QoE)-aware dynamic and reliable framework for AI subtasks composition. The proposed framework is evaluated utilizing three well-known meta-heuristics in terms of various metrics for a connected healthcare application scenario. The experimental results confirm the applicability of the proposed framework. Moreover, the results reveal that black widow optimization (BWO) can handle the issue more efficiently compared to particle swarm optimization (PSO) and simulated annealing (SA). The overall efficiency of BWO over PSO is 95%, and BWO outperforms SA with 100% efficiency. It means that BWO prevails SA and PSO in all and 95% of the experiments, respectively.

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Acknowledgements

This research was supported by the Faculty of Technological Innovation, Zayed University (ZU), under Grant Number RIF-20130.

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Authors and Affiliations

  1. Department of Computer Engineering, Koç University, Istanbul, Turkey

    Vahideh Hayyolalam & Öznur Özkasap

  2. College of Technological Innovation (CTI), Zayed University, Abu Dhabi, United Arab Emirates

    Safa Otoum

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  1. Vahideh Hayyolalam
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Correspondence to Safa Otoum.

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Hayyolalam, V., Otoum, S. & Özkasap, Ö. Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence. Cluster Comput 25, 1695–1713 (2022). https://doi.org/10.1007/s10586-022-03572-9

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  • DOI: https://doi.org/10.1007/s10586-022-03572-9

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