Load balancing method for KDN-based data center using neural network

• Autores: Ruelas, Alex Midwar Rodríguez, Rothenberg, Christian Esteve
• Formato: objeto de conferencia
• Fecha de Publicación: 2019
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/8753
• Enlace del recurso: https://hdl.handle.net/20.500.12724/8753
• Nivel de acceso: acceso abierto
• Materia: Redes neuronales (Informática); Procesamiento electrónico de datos; Neural networks (Computer science); Electronic data processing; Ingeniería de sistemas / Diseño y métodos; http://purl.org/pe-repo/ocde/ford#2.02.04

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.