Abstract
Cloud IoT is a new paradigm that has emerged as a result of the integration of Cloud Computing and the Internet of Things. It provides a set of intelligent services and applications that can strongly influence on our daily lives. In the CloudIoT, physical sensors are responsible for detecting and transmitting data to the cloud in order to be treated and stored. In this context, the amount of data to be processed through the CloudIoT is in increasing usually and, in this situation, the load balancing mechanism is needed in order to distribute the captured data between the different CloudIoT resources. In this paper, we propose an approach that allows to balance the load between the different virtual machines of a CloudIoT. The proposed approach is composed of four essential components: The Classifier which assigns Tasks to its corresponding class and assigns the Virtual Machines (VM) to its corresponding class too, the Dispatcher which sends the tasks to the VM classes, the Local Balancer whose role is to balance the tasks between different VMs belonging to the same class used the spooling method for duplicate same VM, finally, the Manager component or general balancer that is responsible to balance the load between VMs classes with the test of the maximum cloud allowance bar to release VM. The results obtained through the proposed the case study show that our approach allows effectively the load balancing between VM.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Botta, A., Donato, W., Persico, V., Pescapé, A.: Integration of cloud computing and internet of things: a survey. Future Gener. Comput. Syst. 56, 684–700 (2015)
Yezza, A.: La méthode TOPSIS expliquée pas à pas, Sopra Steria Group (2015)
Sidhu, A.K., Kinger, S.: Analysis of load balancing techniques in cloud computing. Int. J. Comput. Technol. 4(2), 737–741 (2013). ISSN 2277-3061
Alakeel, A.M.: A guide to dynamic load balancing in distributed computer systems. Int. J. Comput. Sci. Netw. Secur. 10(6), 153–160 (2010)
Al-Rayis, E., Kurdi, H.: Performance analysis of load balancing architectures in cloud computing. In: IEEE European Modeling Symposium, pp. 520–524 (2013). ISBN 978-1-4799-2577-3
Chien, N.K., Loc, H.D.: Load balancing algorithm based on estimating finish time of services in cloud computing. In: 18th International Conference on Advanced Communication Technology (ICACT), pp. 228–233 (2016). ISBN 978-89-968650-6-3
Mell, P., Grance, T.: The NIST definition of cloud computing. In: The NIST Definition of Cloud Computing, National Institute of Standards and Technology, p. 2 (2009)
Olejnik, R., Alshabani, I., Toursel, B., Laskowski, E., Tudruj, M.: Load balancing metrics for the SOAJA framework. Scalable Comput. Pract. Exp. 10(4), 419–428 (2009)
Kapoor, S., Dabas, C.: Cluster based load balancing in cloud computing. In: Proceedings of the Conference 2015 Eighth International Conference on Contemporary Computing (IC3), pp. 76–81. IEEE, India, 20–22 August 2015
Yakhchi, S., Ghafari, S.M., Yakhchi, M., Fazeli, M., Patooghy, A.: ICA-MMT: A load balancing method in cloud computing environment. IEEE, Sousse (2015)
Hemam, S.M., Hioual, O., Hioual, O.: Load balancing between nodes in a volunteer cloud computing by taking into consideration the number of cloud services replicas. In: Proceedings of the Conference 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), vol. 1, pp. 1–8. IEEE, Morocco, 24–26 October 2017
Cardellini, V., Colajanni, M., Yu, P.S.: Dynamic load balancing on web-server systems. IEEE Internet Comput. 3(3), 28–39 (1999)
Fahim, Y., Benlahmar, E.H., Elhoussine, L., Eddaoui, A.: Une nouvelle conception d’équilibrage de charge dans le cloud computing. In: Proceedings of the conference 4ème Journée sur les Technologies d’Information et de Modélisation TIM 2016, pp. 167–172, Morocco, 02 June 2016
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Benabbes, S., Hemam, S.M. (2020). An Approach Based on (Tasks-VMs) Classification and MCDA for Dynamic Load Balancing in the CloudIoT. In: Hatti, M. (eds) Smart Energy Empowerment in Smart and Resilient Cities. ICAIRES 2019. Lecture Notes in Networks and Systems, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-030-37207-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-37207-1_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37206-4
Online ISBN: 978-3-030-37207-1
eBook Packages: EngineeringEngineering (R0)