Aymen Hasan Alawadi, Maiass Zaher and Sándor Molnár

Methods for Predicting Behavior of Elephant Flows in Data Center Networks

Several Traffic Engineering (TE) techniques based on SDN (Software-defined networking) proposed to resolve flow competitions for network resources. However, there is no comprehensive study on the probability distribution of their throughput. Moreover, there is no study on predicting the future of elephant flows. To address these issues, we propose a new stochastic performance evaluation model to estimate the loss rate of two state-of-art flow scheduling algorithms including Equalcost multi-path routing (ECMP), Hedera besides a flow congestion control algorithm which is Data Center TCP (DCTCP). Although
these algorithms have theoretical and practical benefits, their effectiveness has not been statistically investigated and analyzed in conserving the elephant flows. Therefore, we conducted extensive experiments on the fat-tree data center network to examine the efficiency of the algorithms under different network
circumstances based on Monte Carlo risk analysis. The results show that Hedera is still risky to be used to handle the elephant flows due to its unstable throughput achieved under stochastic network congestion. On the other hand, DCTCP found suffering under high load scenarios. These outcomes might apply to all data center applications, in particular, the applications that demand high stability and productivity.

Infocommunications Journal, Volume XI, Number 3, pp. 34-41., 2019

DOI: 10.36244/ICJ.2019.3.6


Please cite this paper the following way:

Aymen Hasan Alawadi, Maiass Zaher and  Sándor Molnár, "Methods for Predicting Behavior of Elephant Flows in Data Center Networks", Infocommunications Journal, Vol. XI, No 3, September 2019, pp. 34-41. DOI: 10.36244/ICJ.2019.3.6

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