• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

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

Vol. 11, No.11, November 30, 2017
10.3837/tiis.2017.11.004, Download Paper (Free):

Abstract

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.


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Cite this article

[IEEE Style]
Wentao Wang, Lingxia Wang and Fang Zheng, "An Improved Adaptive Scheduling Strategy Utilizing Simulated Annealing Genetic Algorithm for Data Center Networks," KSII Transactions on Internet and Information Systems, vol. 11, no. 11, pp. 5243-5263, 2017. DOI: 10.3837/tiis.2017.11.004

[ACM Style]
Wang, W., Wang, L., and Zheng, F. 2017. An Improved Adaptive Scheduling Strategy Utilizing Simulated Annealing Genetic Algorithm for Data Center Networks. KSII Transactions on Internet and Information Systems, 11, 11, (2017), 5243-5263. DOI: 10.3837/tiis.2017.11.004