Improving big data on research and education networks using future internet approach: A case study of networks analysis

Big Data has emerged as an important platform for data intensive computing. In the complex network infrastructure like UniNet, which is one of the biggest research and education network in Thailand, the network analysis is crucial to operate and maintain the network. Netflow is one of the useful tools for collect data from routers to log files for analyzing the problems. Unfortunately, Netflow generates large amount of data. Therefore, Hadoop platform is selected to implement because it can both store data and process the analysis jobs. Typical Hadoop system operates in a simple network topology and difficult to work in complex network topology like UniNet network, which has two layers: Backbone and Distribution layers. In this case, it needs more than on Hadoop systems working together in this network. Content Centric Network (CCN) is applied to discover network data in the whole network and send the analysis jobs to the particular Hadoop nodes. In principle, OpenFlow enables an application to adjust topology as required by the computation, providing additional network bandwidth to those resources requiring it and also support fault-tolerance when fail-over has happened.