Transitioning an infrastructure of the size of the Internet to a newer technology is no small feat. We are in the midst of such a transition, i.e., from the Internet Protocol version 4 (IPv4) to its version 6 (IPv6). IPv6 was standardized 15 years ago, but until recently there were few incentives to adopt it. The recent allocation of the last large block of IPv4 addresses changed that, and migrating to an IPv6 Internet has become more urgent. This migration is, however, still rife with uncertainties and challenges, and our ability to overcome them can play a major role in both the duration and the cost of a transition that many view as vital to the continued growth of the Internet. In this context, the goals of this research are two-fold.

First, it seeks to provide insight into some of the obstacles that the Internet's transition to IPv6 still faces, and propose possible remedies. The intent is to identify where major roadblocks remain, so as to focus efforts on eliminating them. In particular, the impact of IPv6 connectivity quality on the decisions of content providers, i.e., the likes of Google, YouTube, Yahoo, Hulu, etc., to become directly accessible over IPv6 is critical to a viable IPv6 Internet. Delays in those decisions will not only extend the duration of the transition to an IPv6 Internet, it will make it much more onerous because of the need for expensive gateways. As a result, ensuring that there is no or as few as possible obstacles to adopting IPv6 is of critical importance to the future of the Internet. To realize this goal, the project follows a two-prong approach. The first relies on an extensive set of measurements for characterizing IPv6 adoption across the Internet; in particular in as much as it relates to access to content. The second involves analyzing these measurement data to extract common attributes that contribute to differences in performance between IPv4 and IPv6. Identifying those common attributes will help pinpoint causes of poorer IPv6 performance, when present. Hence, enabling remedies to foster a more complete and more rapid adoption of IPv6.

The second goal of this research is to develop 'models' that can help us better understand and eventually plan future Internet migrations to newer technologies. Those models seek to connect the adoption of a new network technology to the benefits and costs of adopting it, while accounting for the effect of an often dominant incumbent technology. The models that will be investigated rely on methodologies from marketing science and economy, but a key aspect of the research will be to validate those models using the measurement data obtained in the first part of the project. The validation will, therefore, be cast in the context of a migration to IPv6, but the results should have implications for future migrations, including migrations to technologies that offer new and additional capabilities not available from current Internet versions, be they IPv4 or IPv6.

The broader impact of the project is along two fronts. Ensuring that the Internet's migration to its newer version, IPv6, proceeds smoothly can have a significant societal and economical impact. It can facilitate a faster integration of the many devices that are now becoming Internet enabled, and that in the process are fueling the growing need for new Internet addresses, and therefore IPv6. This 'Internet of Things' extends to devices like cars, smart utility meters, sensors monitoring the health of bridges, water supply, etc., that all have a tremendous potential for a safer and cleaner environment. Realizing this potential is, however, heavily depend on a successful migration to IPv6; something that the project hopes to facilitate. Additionally, the development of models to better understand the forces that shape migrations to newer network technologies represents an instance of multi-disciplinary research whose success can foster further collaboration and interactions across disciplines. It can also help train doctoral students better equipped to tackle modern research problems.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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Darleen L. Fisher
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University of Pennsylvania
United States
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