Stochastic Models for Performance Analysis and Cost of a Distributed Architecture Cloud and Fog
Abstract
The fog and cloud computing may perform tasks together to attend different types of applications. However, taking into account variables such as latency, workload and computational capacity, it becomes complex to define under what circumstances it is more advantageous to use the cloud layer or the fog. This paper proposes a Stochastic Petri Net (SPN) to model such a scenario by considering cloud and fog, varying amounts of nodes and workloads. We also present a case study that is a practical guide to infrastructure administrators to adjust their architectures by finding the trade-off between cost and performance.
References
Amarasinghe, G., de Assunc¸ ão, M. D., Harwood, A., and Karunasekera, S. (2018). A data stream processing optimisation framework for edge computing applications. In Proc. of IEEE ISORC.
Borthakur, D., Dubey, H., Constant, N., Mahler, L., and Mankodiya, K. (2017). Smart fog: Fog computing framework for unsupervised clustering analytics in wearable internet of things. In Proc. of IEEE GlobalSIP.
Elkhatib, Y., Porter, B., Ribeiro, H. B., Zhani, M. F., Qadir, J., and Rivi`ere, E. (2017). On using micro-clouds to deliver the fog. arXiv preprint arXiv:1703.00375.
Labadi, K., Benarbia, T., Barbot, J., Hamaci, S., and Omari, A. (2015). Stochastic petri net modeling, simulation and analysis of public bicycle sharing systems. IEEE Transactions on Automation Science and Engineering, 12(4):1380–1395.
Li, H., Ota, K., and Dong, M. (2018). Learning iot in edge: Deep learning for the internet of things with edge computing. IEEE Network, 32(1):96–101.
Li, Y., Orgerie, A., Rodero, I., Parashar, M., and Menaud, J. (2017). Leveraging renewable energy in edge clouds for data stream analysis in iot. In Proc. of IEEE/ACM CCGRID.
Little, J. D. (1961). A proof for the queuing formula: L= w. Operations research, 9(3):383–387.
Mehta, A., T¨arneberg,W., Klein, C., Tordsson, J., Kihl, M., and Elmroth, E. (2016). How beneficial are intermediate layer data centers in mobile edge networks? In Proc. of IEEE FAS*W.
Perera, C., Qin, Y., Estrella, J. C., Reiff-Marganiec, S., and Vasilakos, A. V. (2017). Fog computing for sustainable smart cities: A survey. ACM Comp. Surv., 50(3):32:1–32:43.
Santos, G. L., Takako Endo, P., Ferreira da Silva Lisboa Tigre, M. F., Ferreira da Silva, L. G., Sadok, D., Kelner, J., and Lynn, T. (2018). Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures. Journal of Cloud Computing, 7(1):16.
Souza, V. B., Masip-Bruin, X., Marin-Tordera, E., Ramirez, W., and Sanchez, S. (2016). Towards distributed service allocation in fog-to-cloud (f2c) scenarios. In Proc. of IEEE GLOBECOM).
Vilalta, R., V´ıa, S., Mira, F., Casellas, R., Mu˜noz, R., Alonso-Zarate, J., Kousaridas, A., and Dillinger, M. (2018). Control and management of a connected car using sdn/nfv, fog computing and yang data models. In Proc. of IEEE NetSoft.
Walker, E. (2009). The real cost of a cpu hour. Computer, 42(4):35–41.
Wang, G. and Ng, T. E. (2010). The impact of virtualization on network performance of amazon ec2 data center. In Proc. of IEEE Infocom.
Xu, Y., Mahendran, V., Guo,W., and Radhakrishnan, S. (2017). Fairness in fog networks: Achieving fair throughput performance in mqtt-based iots. In Proc. of IEEE CCNC.
