Abstract
The Internet-of-Things (IoT) generate increasingly significant amount of data that needs to be stored and analysed. The use of IoT devices as a service makes it more accessible and exploitable, this could be achieved using of cloud computing. Multi-cloud service composition and selection are required to fulfill increasingly complicated user requests for services. A service request is made from a cloud broker to cloud providers (CP) to deliver the required Quality of Service (QoS). Selecting services and optimizing service compositions to satisfy functional and non-functional conflicting requirements across various cloud service providers is an non-deterministic polynomial-time hardness problem (NP-hard). Multiobjective (MO) metaheuristics are known to be performant to solve such a problem. This study examines how to select IoT services to achieve the best performances on the eight selected QoS across multiple CP. The experiment results reveal that among the 18 compared algorithms, the parallel NSGAII provides the most efficient and optimal outcomes.
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Zebouchi, A., Aklouf, Y. (2022). A Survey on the Quality of Service and Metaheuristic Based Resolution Methods for Multi-cloud IoT Service Selection. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_40
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