Load balancing method for KDN-based data center using neural network
DOI:
https://doi.org/10.26439/ciis2018.5481Keywords:
OpenFlow, sFlow, data center, artificial neural network, Knowledge-Defined NetworkingAbstract
The growth of cloud application services delivered through data centers with varying traffic demands unveils limitations of traditional load balancing methods. Aiming to attend evolving scenarios and improve the overall network performance, this paper proposes a load balancing method based on an Artificial Neural Network (ANN) in the context of Knowledge-Defined Networking (KDN). KDN seeks to leverage Artificial Intelligence (AI) techniques for the control and operation of computer networks. KDN extends Software-Defined Networking (SDN) with advanced telemetry and network analytics introducing a so-called Knowledge Plane. The ANN is capable of predicting the network performance according to traffic parameters paths. The method includes training the ANN model to choose the path with least load. The experimental results show that the performance of the KDN-based data center has been greatly improved.
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