Advancing Software-Defined Service-Centric Networking Toward In-Network Intelligence

Abstract

Recent advances separately in edge intelligence, service-centric networking, and software-defined networking have profoundly impacted future networks and intelligent applications. Nevertheless, they have not been jointly considered. Jointly considering these three remarkable techniques will open up a nascent research direction. In this article, we propose a software-defined service-centric networking (SDSCN) framework to push the frontier of edge intelligence toward general in-network intelligence. We devise a three-plane architecture, including a data plane, a management plane, and a control plane. Specifically, in-network context-aware computing and caching capabilities are incorporated into the service-centric data plane. Network programmability and global orchestration provide the potential to design plenty of useful network applications to customize network behaviors flexibly. How in-network federated learning works in SDSCN can be observed from case studies and is validated by emulation. More simulation results show the outperformance of SDSCN. Several challenges in developing the proposed framework are presented and discussed.