Abstract
As we know the Internet of Things applications are emerging as a helping hand for the ease of mankind in day-to-day life but when it clubs with cloud computing comes up with the limitation of far distance among Internet of Things gadgets and cloud computing infrastructure which gives an idea to work with a new distributed computing environment with the combination of “cloud computing” and fog computing. “Fog computing” majorly can be used to minimize the transmission delay (latency) and the cost for use of cloud assets as cloud computing helps us to use the complex, large, and heavy tasks to be offloaded on cloud. Here, with this article, we are showing a study for the trade-off between cloud cost and makespan whenever we are scheduling applications in such a kind of environment. We give an algorithm called BAS to sequence applications with the balance between performance and cost of cloud usage. With the simulated results, we have shown that our proposed method is working better compared to some peer methods.
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References
P. Mach, Z. Becvar, Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun. Surv. Tutorials 19, 1628–1656 (2017). https://doi.org/10.1109/comst.2017.2682318
J.D. Ullman, NP-complete scheduling problems. J. Comput. Syst. Sci. 10, 384–393 (1975). https://doi.org/10.1016/S0022-0000(75)80008-0
W.A. Higashino, M.A.M. Capretz, L.F. Bittencourt, CEPSim: modelling and simulation of Complex Event Processing systems in cloud environments. Future Gener. Comput. Syst. 65, 122–139 (2016). https://doi.org/10.1016/j.future.2015.10.023
J.-H. Choi, J. Park, H.D. Park, O. Min, DART: fast and efficient distributed stream processing framework for Internet of Things. ETRI J. 39, 202–212 (2017). https://doi.org/10.4218/etrij.17.2816.0109
A.A. Alsaffar, H.P. Pham, C.-S. Hong et al., An architecture of IoT service delegation and resource allocation based on collaboration between fog and cloud computing. Mob. Inf. Syst. 2016, 1–15 (2016). https://doi.org/10.1155/2016/6123234
H. Topcuoglu, S. Hariri, Wu Min-You, Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13, 260–274 (2002). https://doi.org/10.1109/71.993206
H. Arabnejad, J.G. Barbosa, List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans. Parallel Distrib. Syst. 25, 682–694 (2014). https://doi.org/10.1109/TPDS.2013.57
Z. Wang, Z. Ji, X. Wang et al., A new parallel DNA algorithm to solve the task scheduling problem based on inspired computational model. Biosystems 162, 59–65 (2017). https://doi.org/10.1016/J.BIOSYSTEMS.2017.09.001
F. Bonomi, R. Milito, J. Zhu, S. Addepalli, Fog computing and its role in the internet of things, in Proceedings of the first edition of the MCC workshop on Mobile cloud computing—MCC ’12 (ACM Press, New York, NY, USA, 2012), p. 13
V.B. Souza, X. Masip-Bruin, E. Marin-Tordera et al., Towards distributed service allocation in fog-to-cloud (F2C) scenarios, in 2016 IEEE Global Communications Conference (GLOBECOM) (IEEE, 2016), pp. 1–6
V.B.C. Souza, W. Ramirez, X. Masip-Bruin et al., Handling service allocation in combined Fog-cloud scenarios, in 2016 IEEE International Conference on Communications (ICC) (IEEE, 2016), pp. 1–5
Y. Nan, W. Li, W. Bao et al. Cost-effective processing for delay-sensitive applications in Cloud of Things systems, in 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA) (IEEE, 2016), pp. 162–169
S. Gotoda, M. Ito, N. Shibata, Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault, in 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2012) (IEEE, 2012), pp. 260–267
J. Li, S. Su, X. Cheng et al. Cost-conscious scheduling for large graph processing in the cloud, in 2011 IEEE International Conference on High Performance Computing and Communications (IEEE, 2011), pp. 808–813
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Yadav, A.M., Sharma, S.C., Tripathi, K.N. (2021). A Two-Step Technique for Effective Scheduling in Cloud–Fog Computing Paradigm. In: Gao, XZ., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Computational Intelligence and Communication Technology. Advances in Intelligent Systems and Computing, vol 1086. Springer, Singapore. https://doi.org/10.1007/978-981-15-1275-9_30
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DOI: https://doi.org/10.1007/978-981-15-1275-9_30
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