Abstract
The burst traffic is one of the most important reasons that cause congestion in the data centers. One way to reduce network congestion is to reroute elephant flows on new paths. Most of the current researches focus on the scheme of detecting elephant flows and a few such as Offline Increasing First Fit (OIFF) considers the routing algorithm, which chooses the path with the max remaining bandwidth when scheduling elephant flows. OIFF may relieve the network congestion, but it may also put several elephant flows on the same links which results in new congestions. In this paper, we present Max Probability Fit Algorithm (MPF), a new routing methodology which is based on multinomial logit model (MNL). MPF chooses the rerouting path for each flow with probability, and it’s less likely to distribute the traffic to the same links. The experiment shows that MPF can increase performance of throughput by 3.6% and bring down packet loss rate by 26.84% over OIFF.
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Kreutz, D., Ramos, F.M., Verissimo, P., Rothenberg, C.E., Azodolmolky, S., Uhlig, S.: Software-defined networking: a comprehensive survey. Proc. IEEE 103(1), 14–76 (2015)
Hopps, C.: Analysis of an Equal-Cost Multi-Path Algorithm. RFC 2992, IETF (2000)
Wan, M., Yao, J., Jing, Y., Jin, X.: Event-based anomaly detection for non-public industrial communication protocols in SDN-based control systems. CMC: Comput. Mater. Continua 55(3), 447–463 (2018)
Cheng, R., Xu, R., Tang, X., Sheng, V.S., Cai, C.: An abnormal network flow feature sequence prediction approach for DDoS attacks detection in big data environment. CMC: Comput. Mater. Continua 55(1), 95–119 (2018)
Benson, T., Akella, A., Maltz, D.: Network traffic characteristics of data centers in the wild. In: Proceedings of the l0th ACM SIGCOMM Conference on Internet Measurement, Melbourne, Australia, pp. 267–280 (2010)
Greenberg, A., et al.: VL2: a scalable and Hexible data center network. In: SIGCOMM 2009. LNCS. http://www.springer.com/lncs. Accessed 21 Nov 2016
Al-Fares, M., Radhakrishnan, S., Raghavan, B., Huang, N., Vahdat, A.: Hedera: dynamic flow scheduling for data center networks. In: Proceedings of the NSDI, vol. 10, pp. 19–33. USENIX, Berkeley (2010)
Curtis, A.R., Kim, W., Yalagandula, P.: Mahout: low-overhead datacenter traffic management using end-host-based elephant detection. In: Proceedings of the 2011 IEEE INFOCOM, pp. 1629–1637. IEEE Computer Society Press, Washington (2011)
Train, K.: Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge (2003)
Curtis, A.R., Mogul, J.C., Tourrilhes, J., Yalagandula, P., Sharma, P., Banerjee, S.: Devoflow: scaling flow management for high performance networks. ACM SIGCOMM Comput. Commun. Rev. 41(4), 254–265 (2011)
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Hou, R., Wang, D., Wang, Y., Zhu, Z. (2019). A Congestion Control Methodology with Probability Routing Based on MNL for Datacenter Network. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11635. Springer, Cham. https://doi.org/10.1007/978-3-030-24268-8_32
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DOI: https://doi.org/10.1007/978-3-030-24268-8_32
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