The current Internet architecture focuses on communicating entities, largely leaving aside the information to be ex-changed among them. However, trends in communication scenarios show that WHAT is being exchanged becoming more important than WHO are exchanging information. Van Jacobson describes this as moving from interconnecting ma-chines to interconnecting information. Any change of this part of the Internet needs argumentation as to why it should be undertaken in the first place. In this position paper, we identify four key challenges, namely information-centrism of applications, supporting and exposing tussles, increasing accountability, and addressing attention scarcity, that we believe an information-centric internetworking architecture could address better and would make changing such crucial part worthwhile. We recognize, however, that a much larger and more systematic debate for such change is needed, underlined by factual evidence on the gain for such change.

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Abstract-Several recently proposed multicast protocols use inpacket Bloom filters to encode multicast trees. These mechanisms are in principle highly scalable because no per-flow state is required in the routers and because routing decisions can be made efficiently by simply checking for the presence of outbound links in the filter. Yet, the viability of previous approaches is limited by the possibility of forwarding anomalies caused by false positives inherent in Bloom filters.This paper explores such anomalies, namely (1) packets storms, (2) forwarding loops and (3) flow duplication. We propose stateless solutions that increase the robustness and scalability of Bloom filter based multicast protocols. In particular, we show that the parameters of the filter need to be varied to guarantee the stability of the packet forwarding, and we present a bit permutation technique that effectively prevents both accidental and maliciously created anomalies. We evaluate our solutions in the context of BloomCast, a source-specific inter-domain multicast protocol, using analytical methods and simulations.

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