Abstract
Fog Computing is characterized as an intermediate layer between the Internet of Things layer and the Cloud Computing layer, which pre-processes information closer to the sensors. However, given the increasing demand for numerous IoT applications, even when close to the sensors, Fog nodes tend to be overloaded, compromising the response times of IoT applications that have latency restrictions, and consequently compromising users’ quality experience too. In this work, we investigated ways to mitigate this problem in order to keep Fog Computing with a homogeneous distribution of load, even in heterogeneous environments, through the distribution of tasks among several computational nodes that compose Fog Computing, performing a dynamic load balancing in real time. For this, an algorithm model is presented, which takes into account the dynamics and heterogeneity of the computational nodes of Fog Computing, which allocates the tasks to the most appropriate node according to the policies predefined by the network administrator. Results show that in the proposed work the homogeneous distribution of tasks was achieved between the Fog nodes, and there was a decrease in response times when compared to other proposed solution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347–2376 (2015)
Al Nuaimi, K., Mohamed, N., Al Nuaimi, M., Al-Jaroodi, J.: A survey of load balancing in cloud computing: challenges and algorithms. In: 2012 Second Symposium on Network Cloud Computing and Applications (NCCA), pp. 137–142. IEEE (2012)
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16. ACM (2012)
Casavant, T.L., Kuhl, J.G.: A taxonomy of scheduling in general-purpose distributed computing systems. IEEE Trans. Softw. Eng. 14(2), 141–154 (1988)
Consortium, O.: OpenFog reference architecture for fog computing. Architecture Working Group (2017)
Dey, S., Saha, J.K., Karmakar, N.C.: Smart sensing: chipless RFID solutions for the Internet of Everything. IEEE Microw. Mag. 16(10), 26–39 (2015)
Jadeja, Y., Modi, K.: Cloud computing-concepts, architecture and challenges. In: 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 877–880. IEEE (2012)
Ningning, S., Chao, G., Xingshuo, A., Qiang, Z.: Fog computing dynamic load balancing mechanism based on graph repartitioning. Chin. Commun. 13(3), 156–164 (2016)
Oueis, J., Strinati, E.C., Barbarossa, S.: The fog balancing: load distribution for small cell cloud computing. In: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), pp. 1–6. IEEE (2015)
Padoin, E.L., Navaux, P.O.A., Méhaut, J.F.: Using power demand and residual load imbalance in the load balancing to save energy of parallel systems. In: International Conference on Computational Science (ICCS), Zurich, Switzerland, pp. 1–8 (2017)
Puthal, D., Obaidat, M.S., Nanda, P., Prasad, M., Mohanty, S.P., Zomaya, A.Y.: Secure and sustainable load balancing of edge data centers in fog computing. IEEE Commun. Mag. 56(5), 60–65 (2018)
Subashini, S., Kavitha, V.: A survey on security issues in service delivery models of cloud computing. J. Netw. Comput. Appl. 34(1), 1–11 (2011)
Vaquero, L.M., Rodero-Merino, L.: Finding your way in the fog: towards a comprehensive definition of fog computing. ACM SIGCOMM Comput. Commun. Rev. 44(5), 27–32 (2014)
Verma, M., Bhardwaj, N., Yadav, A.K.: Real time efficient scheduling algorithm for load balancing in fog computing environment. Int. J. Inf. Technol. Comput. Sci. 8(4), 1–10 (2016)
Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42. ACM (2015)
Yin, L., Luo, J., Luo, H.: Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing. IEEE Trans. Ind. Inf. 14(10), 4712–4721 (2018)
Acknowledgements
This research was supported by MCTIC/CNPq - Universal 28/2018 under grants 436339/2018-8 and CAPES-Brazil under grants 1765322. It was also supported by University of Grenoble, Grenoble – France.
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
Pereira, E., Fischer, I.A., Medina, R.D., Carreno, E.D., Padoin, E.L. (2020). A Load Balancing Algorithm for Fog Computing Environments. In: Crespo-Mariño, J., Meneses-Rojas, E. (eds) High Performance Computing. CARLA 2019. Communications in Computer and Information Science, vol 1087. Springer, Cham. https://doi.org/10.1007/978-3-030-41005-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-41005-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41004-9
Online ISBN: 978-3-030-41005-6
eBook Packages: Computer ScienceComputer Science (R0)