Abstract
With an increase in number of services being provided over the Internet, the number of users using these services and the number of servers/Datacentres providing the services have also increased. The use of Fog Computing enhances reliability and availability of these services due to enhanced heterogeneity and increased number of computing servers. However, the users of Cloud/Fog devices have different priority of device type based on the application they are using. Allocating the best Datacentre to process a particular user’s request and then balancing the load among available Datacentres is a widely researched issue. This paper presents a new service broker policy for Fog computing environment to allocate the optimal Datacentre based on users’ priority. Comparative analysis of simulation results shows that the proposed policy performs significantly better than the existing approaches in minimizing the cost, response time and Datacentre processing time according to constraints specified by users.
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References
Bonomi, F.: Connected vehicles, the internet of things, and fog computing. In: 8th ACM International Workshop on Vehicular Inter-Networking (VANET), pp. 13–15. ACM, Las Vegas (2011)
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). doi:10.1145/2342509.2342513
Dastjerdi, A.V., Gupta, H., Calheiros, R.N., Ghosh, S.K., Buyya, R.: Fog computing: principals, architectures, and applications. In: Internet of things: principles and paradigms, 1st edn., Chap. 4, pp. 1–26. Elsevier (2016)
Naha, R.K., Mohamed, O.: Cost-aware service brokering and performance sentient load balancing algorithms in the cloud. J. Netw. Comput. Appl. 75, 47–57 (2016). Elsevier
Ningning, S., Chao, G., Xingshuo, A., Qiang, Z.: Fog computing dynamic load balancing mechanism based on graph repartitioning. IEEE China Commun. 13(3), 156–164 (2016). doi:10.1109/CC.2016.7445510. IEEE
Verma, S., Yadav, A.K., Motwani, D., Raw, R.S., Singh, H.K.: An efficient data replication and load balancing technique for fog computing environment. In: 3rd International Conference on Computing for Sustainable Global Development, pp. 5092–5099. IEEE (2016)
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. 4, 1–10 (2016). doi:10.5815/ijitcs.2016.04.01. MECS Press
Wickremasinghe, B.: CloudAnalyst: a CloudSim-based tool for modeling and analysis of large scale cloud computing environment, MEDC Project Report, University of Melbourne (2009)
Rekha, P.M., Dakshayini, M.: Cost-based Datacentre selection policy for large-scale networks. In: International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC). IEEE, pp. 18–23 (2014). doi:10.1109/ICCPEIC.2014.6915333
Wickremasinghe, B., Calheiros, R.N., Buyya, R.: Cloud analyst: a CloudSim-based visual modeler for analyzing cloud computing environments and applications. In: Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 446–452. IEEE (2010). doi:10.1109/AINA.2010.32
Calheiros, R.N., Ranjan, R., Beloglazov, A., Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw.: Pract. Exp. 41, 23–50 (2011). doi:10.1002/spe.995. Wiley
Manasrah, A.M., Smadi, T., Almomani, A.: A variable service broker routing policy for data center selection in cloud analyst. J. King Saud Univ. - Comput. Inf. Sci. 29(3), 365–377 (2017)
Jaikar, A., Kim, G.R., Noh, S.Y.: Effective Datacentre selection algorithm for a federated cloud. Adv. Sci. Technol. Lett. (Cloud Super Comput.) 35, 66–69 (2013). doi:10.14257/astl.2013.35.16. SERSC
Jaikar, A., Noh, S.Y.: Cost and performance effective Datacentre selection system for scientific federated cloud. Peer-to-Peer Netw. Appl. 8, 896–902 (2015). doi:10.1007/s12083-014-0261-7
Agarwal, S., Yadav, S., Yadav, A.K.: An efficient architecture and algorithm for resource provisioning in fog computing. Int. J. Inf. Eng. Electron. Bus. (IJIEEB) 8, 48–61 (2016). doi:10.5815/ijieeb. MECS Press
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Arya, D., Dave, M. (2017). Priority Based Service Broker Policy for Fog Computing Environment. In: Singh, D., Raman, B., Luhach, A., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2017. Communications in Computer and Information Science, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-10-5780-9_8
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DOI: https://doi.org/10.1007/978-981-10-5780-9_8
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