Content replication and name-based routing lead to a natural multi-source and multipath transmission paradigm in NDN. Due to the unique connectionless characteristic of NDN, current end-to-end multipath congestion control schemes (e.g. MPTCP) cannot be used directly on NDN. This paper proposes a Network Utility Maximization (NUM) model to formulate multi-source and multipath transmission in NDN with in-network caches. From this model, a family of receiver-driven transmission solutions can be derived, named as path-specified congestion control. The path-specified congestion control enables content consumers to separate the traffic control on each path, which consequently facilitates fair and efficient bandwidth sharing amongst all consumers. As a specific instance, a Delay-based Path-specified Congestion Control Protocol (DPCCP) is presented, which utilizes queuing delays as signals to measure and control congestion levels of different bottlenecks. In addition, a set of high-performance congestion control laws are designed to accelerate bandwidth and fairness convergence towards the optimum defined by the NUM model. Finally, DPCCP is compared with state-of-the-art solutions. The experimental evaluations show that DPCCP outperforms existing solutions in terms of bandwidth utilization, convergence time and packet loss.

Hadoop is one of the most important Big Data processing and storage systems. In recent years, a lot of efforts have been put to enhance Hadoop's performance from networking perspectives. However, there are limited tools that can help researchers to verify their networking algorithm design in terms of Hadoop's performance. This paper proposes Doopnet which is a framework and toolset for creating Hadoop clusters in a virtualized environment and for monitoring/analysing of Hadoop's networking characteristics under different network configurations. Doopnet enables users to automatically set up a Hadoop cluster over Docker containers running inside Mininet. The Hadoop traffic is collected inside the containers and virtual switches through network flow monitors. The users can easily modify network topologies or configurations through Mininet, observe the networking behaviour through network flow monitors, and analyse the effects of different network settings on Hadoop's performance. Examples are presented to demonstrate how to setup the Doopnet testbed and analyse Hadoop traffic.