factors and success or failure of protocols that have reached the status of proposed
standard or higher within the IETF. It shows how incorporating our engineering
intuition in the process helps identify key features correlated with success or failure
of protocols. The outcomes of the methodology are models that provide accuracies
almost all categories of RFCs. Also, the findings confirm a number of intuitive
results, as well as (interesting) insight into the impact of less expected features that
our engineering intuition could not afford on its own.
There are however, some limitations and shortcomings associated with our
methodology and findings. In particular, the small sample sizes can potentially
affect not only the accuracy of the classifiers, but also the p-value approximations
requiring large samples, e.g., likelihood ratio test. This is to some extent addressed
by using an exact logistic regression method, and showing that the approximations
are relatively accurate. However, as a future work direction, we seek to expand
the dataset in cases where there are only a small number of samples and the clas-
sifier accuracy is low, e.g., routing protocols, and possibly improve the statistical
significance of our results.
Another potential limitation is that the accuracy of the classifier does not exceed
85%, which can be due to not including a critical factor in our study. We are open
to considering additional factors in our future works given that they meet the cri-
teria defined in Section 4.2, however, it is also likely that the adoption of a protocol
depends on subtle factors that are hard, if not impossible, to measure, e.g.,commer-
cial interest from companies, involvement of influential people in standardization,
etc.
Moreover, we acknowledge that our study shows correlations between success
guarantee causality. In other words, we do not claim that the existence or lack of
the features shown in Section 4.5 cause success or failure. Establishing a causal
relationship is much harder, and requires randomized experiments, etc., which can
be investigated in future research.
Another potential shortcoming is the lack of a separate validation/test dataset,
which can further confirm the outcomes. This is a direction we seek to explore
in the future. In particular, by constructing a dataset from another category of
RFCs, namely, those that were stopped at the Experimental stage by the IETF.
This dataset not only enables us to further validate our results, but also creates
a means to examine the consistency of the IETF decision making in identifying
winning and losing proposals, i.e., IETF has identified the RFCs in this dataset
as “not good enough” to be promoted to the standards track, now this analysis
allows us to compare those decisions with the ones that were indeed promoted to
the standards track.
Finally, we seek to explore the robustness of our findings to other classification
methods. Therefore, another future works direction can include examining non
Chapter 5
5.1
Conclusions
The focus of this dissertation is on understanding how and why different factors af-
fect the adoption of a network technology. To achieve this goal, we investigate three
problems, in the context of the Internet protocols. Initially, we specifically focus
on IPv6, as the arguably the most important instance of protocol adoption in the
Internet’s history. Then learning from that case study, we extend our investigation
to cover a more general Internet protocol. Our contributions involve developing
methodologies for investigating the adoption of network protocols. These method-
ologies include developing measurement tools, models, and statistical hypotheses.
The first part of the thesis investigates the adoption of IPv6 and the path of
its progress over the last two decades. It identifies the key stakeholders and deci-
sion makers that affect the evolution of IPv6 adoption. In particular, ISPs, ICPs,
ITDs, and users are shown to be involved in the adoption of IPv6, either as deci-
sion makers, or as consumers of the technology. It also identifies using empirical
measurements done by us and others, a 3-phase adoption pattern across different
Internet stakeholders. Then it connects those phases to various factors and deci-
sions made by the key Internet stakeholders. Finally, it develops a simple model
to validate the causal relationship between the changes in those factors, and the 3
phases of IPv6 adoption. The models, and the measurement methodologies can be
used in other similar technology adoption instances.
different scenarios can speed up or derail its progress. The investigations involve a
number of representative scenarios, and Internet stakeholders. It captures the de-
cision making process of those Internet stakeholders and the interactions between
them. The findings show that in scenarios where ISPs do not agree to offer IPv6
immediately to their users, a race to attract users can lead to a non-predictable and
haphazard IPv6 adoption dynamics. However, the scenarios which include ISPs
that have a minimal coordination, namely, all offer IPv6 as one of their connectiv-
ity options, the adoption picture becomes clear. Even though the latter scenarios
do not offer any guarantee of IPv6 adoption, they create an environment where the
impact of different factors become clear, therefore, help the Internet stakeholders to
devise strategies. The study also investigates the role of address translation mecha-
nisms, as well as the costs of translation and acquisition of extra IPv4 addresses on
the adoption of IPv6. In particular, the study highlights an unintuitive and surpris-
ing finding, namely, that improvements in the performance of address translation
devices can derail the progress of IPv6 adoption.
The above studies raise a question about the adoption dynamics of other pro-
tocols (or network technologies), and the impact of various factors on them. This
endeavor is documented as the third part of this thesis. The study focuses on the
protocols in the standards track of the Internet Engineering Task Force (IETF).
First, we collect data to properly characterize those protocols using a number of
tical tools, such as binary logistic regression, the correlation between the success
of a protocol and the features that characterize them is investigated. This reveals
a number of intuitive findings as well as some less expected ones. In particular,
the findings confirm a number of anecdotal evidences, such as the positive impact
of backward compatibility on the adoption of a protocol extension or version, or
the negative impact of security as the motivation of a protocol. However, they also
provide new insight on how generating additional value boosts the success of a new
protocol, or how belonging to a specific protocol type/functionality changes the
dynamics of adoption. In addition to providing the insight and formally confirm-
ing a number of anecdotal evidence about protocol adoption, the study develops
a framework and a methodology on how to use statistical tools to understand the
dynamics of a (general) technology adoption.
Next, we propose directions and potential future works that can improve the
models and frameworks developed in this dissertation.