A Load Balancing Method based on Artificial Neural Networks for Knowledge-defined Data Center Networking
Alex Midwar Rodriguez Ruelas, Christian Esteve Rothenberg · 2018
The growth of cloud application services delivered through data centers with varying traffic demands unveils limitations of traditional load balancing methods. Aiming at attending evolving scenarios towards improved network performance, this paper presents 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 proposed ANN is capable of predicting the network performance according to traffic parameters by creating a model of traffic behavior based on bandwidth and latency measurements over different paths. The method includes training the ANN model to choose the path with least load. We conduct a series of experiments in an emulated environment to validate the proposed method. The initial experimental results point to the potential performance gains of KDN approaches.