Abstract
Software-defined networking (SDN) is a revolutionary technology that facilitates network management and enables programmatically efficient network configuration, thereby improving network performance and flexibility. However, as the application programming interfaces (APIs) of SDN are low-level or functionality-restricted, SDN programmers cannot easily keep pace with the ever-changing devices, topologies, and demands of SDN. By deriving motivation from industry practice, we define a novel network algorithm programming language (NAPL) that enhances the SDN framework with a rapid programming flow from topology-based network models to C++ implementations, thus bridging the gap between the limited capability of existing SDN APIs and the reality of practical network management. In contrast to several state-of-the-art languages, NAPL provides a range of critical high-level network programming features, (1) topology-based network modeling and visualization; (2) fast abstraction and expansion of network devices and constraints; (3) a declarative paradigm for the fast design of forwarding policies; (4) a built-in library for complex algorithm implementation; (5) full compatibility with C++ programming; and (6) userfriendly debugging support when compiling NAPL into highly readable C++ codes. The expressiveness and performance of NAPL are demonstrated in various industrial scenarios originating from practical network management.
Jian Wang
Ph.D. student in Computer Vision
My research interests encompass a broad spectrum of human motion and egocentric vision. Recently I am focusing on the egocentric human motion capture.