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A collaborative resource management for big IoT data processing in Cloud

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Abstract

These days, handling large amounts of data generated from Internet of Things (IoT) applications in the Cloud has turned into a powerful solution for fulfilling Quality of Service requests from clients. However, to save on costs, the union of cloud providers, known as a cloud confederation, can be a promising methodology because this organization helps cloud suppliers to overcome the restrictions of physical assets in handling Big IoT Data. Nonetheless, the key challenge is to discover appropriate cloud collaborators to form a confederation that will achieve the required level of services characterized in service level agreements. In this paper, to execute heterogeneous Big IoT Data handling demands from clients, we build a cloud confederation model that determines ideal choices for target cloud providers. In addition, we present a multi-objective (MO) optimization model of collaborator selection among different clouds. To solve the MO optimization model, a general structure for a multi-objective genetic algorithm is also developed. The proposed model is tested through various test assessments.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia for funding this work through the research group Project No RGP-VPP-318.

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  1. Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Abdulhameed Alelaiwi

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  1. Abdulhameed Alelaiwi
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Alelaiwi, A. A collaborative resource management for big IoT data processing in Cloud. Cluster Comput 20, 1791–1799 (2017). https://doi.org/10.1007/s10586-017-0839-y

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