On Using Flow Classification to Optimize Traffic Routing in SDN Networks

Abstract

Cloud storage services are gaining a widespread popularity thanks to their scalability and performance. Several companies and users are relying on such services to store and retrieve their files. In this context, the file transfer time is critical for users' satisfaction. This time can be minimized by carefully selecting the routing strategy to transfer the flow of packets associated with each file. In this paper, we introduce a novel class-based routing strategy called LUNA that is able to minimize the flow completion time. LUNA classifies the flows into mice and elephants based on their size. Afterwards, it leverages a machine learning technique called association rules to generate the forwarding rules and route each flow based on its class (i.e., mouse or elephant). Experimental results show that LUNA has successfully identified the class of 80% of the flows. Furthermore, its class-based routing outperforms basic routing strategies in terms of flow completion time, throughput and packet loss by almost 47%, 41% and 23%, respectively.