Software-defined extreme scale networks for bigdata applications

Abstract

Software-Defined Networking (SDN) is an emerging technology that supports recent network applications. An SDN redefines networks by introducing the concept of decoupling the control plane from the data plane, thus providing centralized management, programmability, and dynamic reconfiguration. In this research, we specifically investigate the significance of using SDNs in support of Big-Data applications. SDN proved to support Big-Data applications through a more efficient use of distributed nodes. With Hadoop as an example of Big-Data application, we investigate the performance in terms of throughput and execution time for the read/write and sorting operations. The experiments take into consideration different network sizes of a Fat-tree topology. A Hadoop multi-node cluster is installed in Docker containers connected through a Fat-tree of OpenFlow switches. The packet forwarding is either by way of an SDN controller or the normal packet switching rules. Experimental results show that using an SDN controller outperforms normal forwarding by the switches. As a result, our research suggests that using SDN controllers has a strong potential to greatly enhance the performance of Big-Data applications on extreme-scale networks.