## 1 Introduction  
There has been a number of challenges emerged to large-scale data movement after the emergence of extreme-scale scientific applications. The data transmission is an essential function for scientific discoveries, particularly within big data environments. To date, there are several data transfer tools (such as GridFTP [1] and BBCP [2]) and services (such as the PhEDEx high-throughput data transfer management system, Globus Online [3], and the LIGO Data Replicator) have been developed to support the large-scale data transfer. These tools and services implemented the number of advanced data transfer features, such as partial transfer, transfer resumption, third-party transfer, and security. Although significant improvements have been made in large-scale data movement, but the existing data transferring tools are not up to the mark. These tools and services will require out of the box solutions to successfully meet the large-scale data transfer challenges. The existing network paradigm that supports extreme-scale distributed science workflows consists of three major components: terabit networks that provide high network bandwidths, data transfer nodes (DTNs) and science DMZ architecture [4; 5; 6] that bypasses performance loopholes of scientific campus networks. On the other hand, Internet2 AL2S [7] and ESNet OSCARS [8] are mainly responsible for on-demand secure path reservation over the Wide Area Network (WAN), and these solutions also provide the automated, guaranteed bandwidth services for scientific workflows. The existing network paradigm for scientific workflows has been proven very successfully. However, it has been observed during our comprehensive study that the present network paradigm for an extreme-scale scientific data transfer job still needs some more improvements to reach its full potentials. We claimed that the current networking paradigm must address the following major problems: scalability, last mile, and dynamic programmability problems. The recent emerging concept in the network world is called Software-Defined Networking (SDN) [9; 10; 11]. This latest technology has been providing the new methods of configuration and management of networks. In SDN, the underlying network devices are simply considered as packets forwarding elements. It is possible to manage the control logic of the network centrally through a software program, which can effectively control the entire network behavior. In order to address such problems mentioned above, AmoebaNet [12] has been proposed. It is common to notice low efficiency in data movement when running the data transfer tools on DTN machines. Such inefficient data transmission is one of critical points in scientific data computing. Therefore, it is required to provide high-performance, predictable, and schedulable data movement services for large-scale scientific facilities and their collaborators. Such challenges can be resolved by AmoebaNet, a Software-Defined Network (SDN) enabled service, which allows application to dynamically program the networks at run-time for bulk data transfers. AmoebaNet applies SDN technology to provide \"QoS-guaranteed\" network services in campus or local area networks. It is also possible that AmoebaNet complements existing network paradigm for extreme-scale distributed science: it allows applications to dynamically program the networks at run-time for optimum performance; and, it can be easily integrated with WAN circuit/path reservation systems such as ESNet OSCARS and Internet2 AL2S; Such capabilities of AmoebaNet can solve the problems of scalability, last mile, and dynamic programmability. In this paper, we particularly focused on an extension of AmoebaNet with some latest development of dynamic programmability. We evaluated AmoebaNet's feature in real world test cases along with a scientific application called BigData Express (BDE).