On Broadcast-based Self-learning in Named Data Networking
by Junxiao Shi, Eric Newberry, and Beichuan Zhang.
IFIP Networking, June 2017.
In local area networks and mobile ad-hoc networks,
broadcast-based self-learning is a common mechanism to find
packet delivery paths. Self-learning broadcasts the first packet,
observes where the returning packet comes from, then creates
the corresponding forwarding table entry so that future packets
will only need unicast. The main benefits of this mechanism are
its simplicity, adaptability, and support of mobility. While the
high-level idea of broadcast-based self-learning is straightforward,
making the scheme efficient and secure, especially in a
data-centric network architecture like Named Data Networking
(NDN), requires careful examination. In this paper, we study how
broadcast-based self-learning may be applied to NDN networks,
point out two major issues: the name-prefix granularity problem
and the trust problem, and propose corresponding solutions. We
also apply self-learning to switched Ethernet as an example to
develop a specific design that can build forwarding tables without
any control protocol, recover quickly from link failures, and make
use of off-path caches. Simulations are conducted using both real
and synthetic traffic to evaluate the performance of the design.