An Improved Adaptive Scheduling Strategy Utilizing Simulated Annealing Genetic Algorithm for Data Center Networks

作者：Wang, Wentao^*; Wang, Lingxia; Zheng, Fang

来源：KSII Transactions on Internet and Information Systems, 2017, 11(11): 5243-5263.

DOI：10.3837/tiis.2017.11.004

• scheduling
• adaptive methods
• simulated annealing genetic algorithm
• software defined network
• data center

Data center networks provide critical bandwidth for the continuous growth of cloud computing, multimedia storage, data analysis and other businesses. The problem of low link bandwidth utilization in data center network is gradually addressed in more hot fields. However, the current scheduling strategies applied in data center network do not adapt to the real-time dynamic change of the traffic in the network. Thus, they fail to distribute resources due to the lack of intelligent management. In this paper, we present an improved adaptive traffic scheduling strategy utilizing the simulated annealing genetic algorithm (SAGA). Inspired by the idea of software defined network, when a flow arrives, our strategy changes the bandwidth demand dynamically to filter out the flow. Then, SAGA distributes the path for the flow by considering the scheduling of the different pods as well as the same pod. It is implemented through software defined network technology. Simulation results show that the bisection bandwidth of our strategy is higher than state-of-the-art mechanisms.

• 出版日期2017-11-30
• 单位中南民族大学