  In order to improve future network performance, this paper proposes scalable
intelligence-enabled networking (SIEN) with eliminating traffic redundancy for
audio-visual-tactile Internet in 5G scenarios such as enhanced mobile
broadband, ultra-reliable and low latency communication, and massive
machine-type communication. The SIEN consists of an intelligent management
plane (ImP), an intelligence-enabled plane (IeP), a control plane and a user
plane. For the ImP, the containers with decision execution are constructed by a
novel graph algorithm to organize objects such as network elements and resource
partitions. For the IeP, a novel learning system is designed with decision
making using a congruity function for generalization and personalization in the
presence of imbalanced, conflicting and partial data. For the control plane, a
scheme of identifier-locator mapping is designed by referring to
information-centric networking and software-defined networking. For the user
plane, the registrations, requests and data are forwarded to implement the SIEN
and test its performance. The evaluation shows the SIEN outperforms four
state-of-the-art techniques for redundant traffic reduction by up to 46.04%
based on a mix of assumption, simulation and proof-of-concept implementation
for audio-visual-tactile Internet multimedia service. To confirm the validity,
the best case and the worst case for traffic offloading are tested with the
data rate, the latency and the density. The evaluation only focused on the
scalability issue, while the SIEN would be beneficial to improve more issues
such as inter-domain security, ultra-low latency, on-demand mobility,
multi-homing routing, and cross-layer feature incongruity.
