the automated matching of subscriptions/interests to content descriptions. Content is coded as it moves through the nodes in the network. A snapshot of “packets” (on an edge or stored in a node) at any given point in the graph would show they contain a coded multiplex of multiple sources of data. Hence, there would be a poor fit throughout the network architecture for packets, flow-level descriptions, normal capacity assignments, and end-to-end and hop-by-hop protocols. The data is in some sense a shifting interference pattern that emerges from the mixing and merging of all sources.

Have we also unintentionally thrown out the legacy system with the new paradigm? What about person-to-person voice calls and its 21st century equivalent, real-time gaming? If we could push the idea of swarms or waves down into the network architecture, how would the architecture implement cir-cuit-on-a-wave and IP-on-a-wave?

Network architects could do this the same way (inefficiently) they implement VoIP—through a circuit on IP. One is at liberty to run multiple legacy networks, supporting one-to-one flows using separate communications systems, especially since the networks are available already. On the other hand, how would they be supported on the wave? Perhaps through some minimalist publication-and-subscrip-tion system.

Other ways to understand this design concept are circulating in the research community. One is the data-orientated paradigm in which information is indexed by keys and retrieved by subscription. Protocols are declarative. All nodes are caches of content, indexes, and buffers. All nodes forward information while caching, in the style of mobile ad hoc, delay-tol-erant, and peer-to-peer systems; these communication methods are unified in the data-oriented paradigm.

No network architect interested in developing a grand unified network architecture would be concerned with micromanaging fine-grain resources. For a network architect, efficiency is measured at the global level. Traditional activities may be maddeningly inefficient, but most content—video, audio, and sensor data—is handled with maximum efficiency. Content is also handled through multi-path, coded delivery, with good isolation and protection properties through the statistics of scaling, not by virtue of local resource reservation.

So, unlike traditional network architectural goals, the wave-particle duality model I’ve described here pursues a different primary goal. In it, the notion of a wave is optimized for resilience through massive scale, not for local efficiency. Moreover, it supports group communication and mobility naturally, since the ren-

dezvous in the network between publish and consumption is dynamic, not through the coordination of end-points in the classical end-to-end approach.

The details of the wave model are likely to keep researchers busy for the next 20 years. My aim here is to get them to think outside the end-to-end communications box in order to solve the related problems, if they are indeed the right problems, or to propose a better problem statement to begin with.

One might ask many questions about future wave-particle network architecture, including: What is the role of intermediate and end-user nodes? How do they differ? Where would be the best locus for a rendezvous between publication and consumption? Would each rendezvous depend on the popularity of content and its distance from the publisher, subscriber, or mid-point. What codes should be used? How can we build optical hardware to achieve software re-coding? What role might interference in radio networks play in the wave-particle network model? How can we achieve privacy for individual users and their communications in a network that mixes data packets?

This future wave-particle duality in the Internet-based network would be more resilient to failure, noise, and attack than the current architecture where ends and intermediate nodes on a path are sitting ducks for attacks, whether deliberate or accidental. How might its architects quantify the performance of such a system? Do they need a new set of modeling tools—replacing graph theory and queuing systems— to describe it? Finally, if network control is indeed a distributed system, can the idea of peer-to-peer be used as a control plane?

I encourage you not to take my wave-particle duality analogy too seriously, especially since I am suspicious of any grand unified network model myself. But I do encourage you to use the idea to disrupt your own thinking about traditional ideas. In the end, perhaps, we will together discover that many traditional ideas in networking are fine as is, but all are still worth checking from this new perspective. ^c

REFERENCES

1. Anderson, C. The Long Tail: How Endless Choice Is Creating Unlimited Demand. Random House, New York, 2006.

2. Braden, R., Faber, T., and Handley, M. From protocol stack to protocol heap: Role-based architecture. ACM SIGCOMM Computer Communication Review 33, 1 (Jan. 2003), 17– 22.

3. Stephenson, N. The Diamond Age: Or, a Young Lady’s Illustrated Primer. Bantam Spectra Books, New York, 1995.

JON CROWCROFT ( [e-mail]) is the Marconi Professor of Communications Systems in the Computer Laboratory at the University of Cambridge, Cambridge, U.K.