Abstract
This research introduce a robust optimization model to reduce the congestion ratio in communications network considering uncertainty in the traffic demands. The propose formulation is depended on a model called the pipe model. Network traffic demand is fixed in the pipe model and most of the previous researches consider traffic fluctuation locally. Our proposed model can deal with fluctuation in the traffic demands and considers this fluctuation all over the network. We formulate the robust optimization model in the form of second-order cone programming (SOCP) problem which is tractable by optimization software. The numerical experiments determine the efficiency of our model in terms of reducing the congestion ratio compared to the others model.
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References
Das, B.C., Takahashi, S., Oki, E., Muramatsu, M.: Network congestion minimization models based on robust optimization. IEICE Trans. Commun. E101-B(3), 772–784 (2018). https://doi.org/10.1587/transcom.2017EBP3193
Oki, E., Iwaki, A.: Performance comparisons of optimal routing by pipe, hose and intermediate models. In: Proceedings of IEEE Sarnoff Symposium (Sarnoff 2009), pp. 1–5, March/April 2009. https://doi.org/10.1109/SARNOF.2009.4850317
Xu, J., Yang, J.Z., Guo, C., Lee, Y.-H., Lu, D.: Routing algorithm of minimizing maximum link congestion on grid networks. Wireless Netw. 21(5), 1713–1732 (2014). https://doi.org/10.1007/s11276-014-0878-8
Wang, Y., Wang, Z.: Explicit routing algorithms for internet traffic engineering. In: IEEE International Conference on Computer Communications and Networks (ICCCN) (1999). https://doi.org/10.1109/ICCCN.1999.805577
Juttner, A., Szabo, I., Szentesi, A.: On bandwidth efficiency of the hose resource management model in virtual private networks. In: IEEE Infocom 2003, pp. 386–395, March/April (2003). https://doi.org/10.1109/INFCOM.2003.1208690
Duffield, N.G., Goyal, P., Greenberg, A., Mishra, P., Ramakrishnan, K.K., Merwe, J.E.: Resource management with hose: point-to-cloud services for virtual private networks. IEEE/ACM Trans. Netw. 10(5), 679–692 (2002). https://doi.org/10.1109/TNET.2002.803918
Kumar, A., Rastogi, R., Silberschatz, A., Yener, B.: Algorithms for provisioning virtual private networks in the hose model. IEEE/ACM Trans. Networking 10(4), 565–578 (2002). https://doi.org/10.1109/TNET.2002.802141
Li, C., Xie, R., Huang, T., Liu, Y.: Jointly optimal congestion control, forwarding strategy and power control for named-data multihop wireless network. IEEE Access 5, 1013–1026 (2017). https://doi.org/10.1109/ACCESS.2016.2634525
Singhal, P., Yadav, A.: Congestion detection in wireless sensor network using neural network. In: International Conference for Convergence for Technology-2014, April 2015. https://doi.org/10.1109/I2CT.2014.7092259
Das, B.C., Oki, E., Muramatsu, M.: Comparative performance of green pipe, green hose, and green hose-rectangle models in power efficient network. In: IEEE ComSoc International Communications Quality and Reliability Workshop, pp. 1–6 (2018). https://doi.org/10.1109/CQR.2018.8445921
Chu, J., Lea, C.: Optimal link weights for maximizing QoS traffic. In: IEEE ICC 2007, pp. 610–615 (2007). https://doi.org/10.1109/ICC.2007.105
Chu, J., Lea, C.: Optimal link weights for maximizing QoS traffic. IEEE/ACM Trans. Netw. 17(3), 778–788 (2009)
Boyd, S., Vandenberghe, L.: Approximation and Fitting in Convex Optimization, pp. 318–324. Cambridge University Press, Cambridge (2005)
Pedroso, J.P., Rais, A., Kubo, M., Muramatsu, M.: Second-order cone optimization. In: Mathematical Optimization: Solving Problems Using Gurobi and Python, pp. 108–115, September 2012
Das, B.C., Oki, E., Muramatsu, M.: A simple SOCP formulation of minimization of network congestion ratio, RIMS Kokyuroku 2027, Kyoto University, Kyoto, Japan, vol. 2027, pp. 52–59, April 2017
Nesterov, Y., Nemirovsky, A.: Interior-point polynomial methods in convex programming. Studies in Applied Mathematics, vol. 13. SIAM, Philadelphia (1994)
Boyd, S., Vandenberghe, L.: Interior-point methods. In: Convex Optimization. Cambridge University Press, Cambridge (2005), chap. 11, sect. 11.7, pp. 609–614 (2005)
Gurobi Optimization. Verison: 6.5.2, October Sky (2015). http://www.gurobi.com
Solving Constraint Integer Programs. http://scip.zib.de
Explore IBM software and Solutions. https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/
Ouedraogo, I.A., Oki, E.: A green and robust optimization strategy for energy saving against traffic uncertainty. IEEE J. Sel. Areas Commun. 34(5), 1405–1416 (2016). https://doi.org/10.1109/JSAC.2016.2545378
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Das, B.C., Begum, M., Uddin, M.M., Rahman, M.M. (2020). Conic Programming Approach to Reduce Congestion Ratio in Communications Network. In: Bhuiyan, T., Rahman, M.M., Ali, M.A. (eds) Cyber Security and Computer Science. ICONCS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-52856-0_45
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