internetmap – ANT Research News

We have released a new technical report: “Towards a Non-Binary View of IPv6 Adoption”, available at https://arxiv.org/abs/2507.11678.

From the abstract:

Breakdown of domains hosted by major cloud providers into IPv4-only (red), IPv6-only (black) and IPv6-full, i.e., IPv4+IPv6 (blue). See Section 5 of the technical report for details. (Figure 10 from the paper.)

Twelve years have passed since World IPv6 Launch Day, but what is the current state of IPv6 deployment? Prior work has examined IPv6 status as a binary: can you use IPv6, or not? As deployment increases we must consider a more nuanced, non-binary perspective on IPv6: how much and often can a user or a service use IPv6? We consider this question as a client, server, and cloud provider. Considering the client’s perspective, we observe user traffic. We see that the fraction of IPv6 traffic a user sends varies greatly, both across users and day-by-day, with a standard deviation of over 15%. We show this variation occurs for two main reasons. First, IPv6 traffic is primarily human-generated, thus showing diurnal patterns. Second, some services are IPv6-forward and others IPv6-laggards, so as users do different things their fraction of IPv6 varies. We look at server-side IPv6 adoption in two ways. First, we expand analysis of web services to examine how many are only partially IPv6 enabled due to their reliance on IPv4-only resources. Our findings reveal that only 12.5% of top 100k websites qualify as fully IPv6-ready. Finally, we examine cloud support for IPv6. Although all clouds and CDNs support IPv6, we find that tenant deployment rates vary significantly across providers. We find that ease of enabling IPv6 in the cloud is correlated with tenant IPv6 adoption rates, and recommend best practices for cloud providers to improve IPv6 adoption. Our results suggest IPv6 deployment is growing, but many services lag, presenting a potential for improvement.

This technical report is a joint work of Sulyab Thottungal Valapu from USC, and John Heidemann from USC/ISI. This work was partially supported by the NSF via the PIMAWAT and InternetMap projects.

The paper “Quantifying Differences Between Batch and Streaming Detection of Internet Outages” will appear in the 2025 Conference on Network Traffic Measurement and Analysis (TMA) June 10-13, 2025 in Copenhagen, Denmark. The batch and streaming datasets are available for download.

Visual representation of outages from 2021-03-01T22:00Z to 2021-03-03T20:00Z from batch and streaming datasets (Figure 3 from [Stutz23a])

From the paper’s abstract:

A number of different systems today detect outages
in the IPv4 Internet, often using active probing and algorithms
based on Trinocular’s Bayesian inference. Outage detection
methods have evolved, both to provide results in near-real-time,
and adding algorithms to account for important but less common
cases that might otherwise be misinterpreted. We compare two
implementations of active outage detection to see how choices
to optimize for near-real-time results with streaming compare
to designs that use long-term information to maximize accuracy
using batch processing. Examining 8 days of data, starting on
2021-02-26, we show that the two similar systems agree most of
the time, more than 84%. We show that only 0.2% of the time the
algorithms disagree, and 15% of the time only one reports. We
show these differences occur due to streaming’s requirement for
rapid decisions, precluding algorithms that consider long-term
data (days or weeks). These results are important to understand
the trade-offs that occur when balancing timely results with
accuracy. Beyond the two systems we compare, our results
suggest the role that algorithmic differences can have in similar
but different systems, such as the several implementations of
Trinocular-like active probing today.

Live data from Trinocular streams in to our outage website 24×7. The specific data used in this paper is available from our website.

This work is partially supported by the project “CNS Core: Small: Event Identification and Evaluation of Internet Outages (EIEIO)” (CNS-2007106) through the U.S. National Science Foundation, and by an REU supplement to that project. Erica Stutz began this work at Swarthmore College, working remotely for the University of Southern California; her current affiliation is Yale University.