Affiliations: College of Computer & Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
Correspondence:
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Corresponding author. Honghui Li, College of Computer & Information Engineering, Inner Mongolia Agricultural University, Hohhot, China. E-mail: lihh@imau.edu.cn.
Abstract: With the rapid development of data center network, the traditional traffic scheduling method can easily cause problems such as link congestion and load imbalance. Therefore, this paper proposes a novel dynamic flow scheduling algorithm GA-ACO (Genetic Algorithm and Ant COlony algorithms). GA-ACO algorithm obtains the global perspective of the network under the SDN (Software defined network) architecture. It then calculates the global optimal path for the elephant flow on the congestion link, and reroutes it. Extensive experiments have been executed to evaluate the performance of the proposed GA-ACO algorithm. The simulation results show that, in comparison with ECMP and ACO-SDN algorithm, GA-ACO can not only reduce the maximum link utilization, but also improve the bandwidth effectively.
Keywords: Data center network, SDN, elephant flow scheduling, genetic algorithm, ant colony algorithm
