Mining Historical Data towards Interference Management in Wireless SDNs

Abstract

WiFi networks are o en planned to reduce interference through planning, macroscopic self-organization (e.g. channel switching) or network management. In this paper, we explore the use of historical data to automatically predict tra c bo lenecks and make rapid decisions in a wireless (WiFi-like) network on a smaller scale. is is now possible with so ware de ned networks (SDN), whose controllers can have a global view of tra c ows in a network. Models such as classi cation trees can be used to quickly make decisions on how to manage network resources based on the quality needs, service level agreement or other criteria provided by a network administrator. e objective of this paper is to use data generated by simulation tools to see if such classi cation models can be developed and to evaluate their e cacy. For this purpose, extensive simulation data were collected and data mining techniques were then used to develop QoS prediction trees. Such trees can predict the maximum delay that results due to speci c tra c situations with speci c parameters. We evaluated these decision/classi cation trees by placing them in an SDN controller. OpenFlow cannot directly provide the necessary information for managing wireless networks so we used POX messenger to set up an agent on each AP for adjusting the network. Finally we explored the possibility of updating the tree using feedback that the controller receives from hosts. Our results show that such trees are e ective and can be used to manage the network and decrease maximum packet delay.