A Software Defined Network Scheme for Intra Datacenter Network Based on Fat-Tree Topology

Abstract

Nowadays datacenters face a big challenge, because the development of cloud services requests a large amount of capital input, high maintenance skills and cost. The major disadvantages of current datacenter network include the high cost of equipment and maintenance, QoS, failure recovery time, network virtualization etc. In this paper, we proposed a software defined datacenter network. The programmable controller is responsible for the management of the traffic scheduling including topology discovery, routing and Quality of Service. The SDN is based on Fat-tree network architecture and max-min fairness. A Genetic Algorithm optimized Radial Basis Function neural network is used to compute the service weight for maximizing the utilization of network resource. An experiment of the network bearing different kinds of services based on Openflow is described. This well-designed fat-tree network has high efficiency in traffic distribution, and has integrated the traditional network architecture and new SDN technology.

A Research Review of OpenFlow for Datacenter Networking

“…In proposal [17], a fat-tree DC network is defined, using OF and a basic ''D-mod-k'' algorithm to distribute traffic (where D is the destination and k is the fat-tree parameter). However, this simple route selection algorithm may not be appropriate if high priority traffic is sent when the network is congested.…”