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network outages in Louisiana with Hurricane Ida

We’ve been watching the situation in Louisiana develop with Hurricane Ida with our Trinocular Internet outage detection system.

Internet outages in Louisiana at 8:30pm Sunday evening August 29, corresponding to Hurricane Ida’s landfall.

Data as of 2021-08-30t01:30Z, which is 8:30pm Sunday night August 29 in New Orleans, shows about half of the networks in the New Orleans area being unreachable (mostly IPv4 home networks). Following shortly after landfall, these outages correspond with news reports about widespread power loss. Current data is appearing on our Internet outage map.

We wish the residents of Louisiana the best and hope for a rapid recovery.

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ANT research group lunch

At the end of June we had an ANT research group lunch to celebrate four (!) recent PhD defenses in 2020 and 2021: Hang Guo, Calvin Ardi, Lan Wei, and Abdul Qadeer. Although not everyone could be there (Hang has already moved for his new job), and the ANT lab includes a number of people outside of L.A. who could not make it, us students, staff, and family in L.A. had a great time at Vista del Mar Park near the beach!

A big thanks to Basileal Imana and ASM Rizvi for coordinating delivery of Ethiopian food for lunch.

We are also very thankful that vaccine availability in the U.S. is widespread and we were able to get together face-to-face after a year of Covid limitations. I’m happy that we’ve been able to do good work throughout the pandemic with remote collaboration tools and occasional on-site access, but it was nice to see old friends face-to-face again and share a meal. We hope the fall’s in-person classes at USC go well.

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DNS Papers Publications

New paper and talk “Institutional Privacy Risks in Sharing DNS Data” at Applied Networking Research Workshop 2021

Basileal Imana presented the paper “Institutional Privacy Risks in Sharing DNS Data” by Basileal Imana, Aleksandra Korolova and John Heidemann at Applied Networking Research Workshop held virtually from July 26-28th, 2021.

From the abstract:

We document institutional privacy as a new risk
posed by DNS data collected at authoritative servers, even
after caching and aggregation by DNS recursives. We are the
first to demonstrate this risk by looking at leaks of e-mail
exchanges which show communications patterns, and leaks
from accessing sensitive websites, both of which can harm an
institution’s public image. We define a methodology to identify queries from institutions and identify leaks. We show the
current practices of prefix-preserving anonymization of IP
addresses and aggregation above the recursive are not sufficient to protect institutional privacy, suggesting the need for
novel approaches.

Number of MX and DNSBL queries in a week-long root DNS data that can potentially leak email-related activity

The data from this paper is available upon request, please see our project page.

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new conference paper “Anycast in Context: A Tale of Two Systems” at SIGCOMM 2021

We published a new paper “Anycast in Context: A Tale of Two Systems” by Thomas Koch, Ke Li, Calvin Ardi*, Ethan Katz-Bassett, Matt Calder**, and John Heidemann* (of Columbia, where not otherwise indicated, *USC/ISI, and **Microsoft and Columbia) at ACM SIGCOMM 2021.

From the abstract:

Anycast is used to serve content including web pages and DNS, and anycast deployments are growing. However, prior work examining root DNS suggests anycast deployments incur significant inflation, with users often routed to suboptimal sites. We reassess anycast performance, first extending prior analysis on inflation in the root DNS. We show that inflation is very common in root DNS, affecting more than 95% of users. However, we then show root DNS latency hardly matters to users because caching is so effective. These findings lead us to question: is inflation inherent to anycast, or can inflation be limited when it matters? To answer this question, we consider Microsoft’s anycast CDN serving latency-sensitive content. Here, latency matters orders of magnitude more than for root DNS. Perhaps because of this need, only 35% of CDN users experience any inflation, and the amount they experience is smaller than for root DNS. We show that CDN anycast latency has little inflation due to extensive peering and engineering. These results suggest prior claims of anycast inefficiency reflect experiments on a single application rather than anycast’s technical potential, and they demonstrate the importance of context when measuring system performance.

Tom also blogged about this work at APNIC.

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new conference paper “Efficient Processing of Streaming Data using Multiple Abstractions” at IEEE Cloud

We have published a new paper “Efficient Processing of Streaming Data using Multiple Abstractions” at the IEEE Cloud 2021 conference. (to be available at https://conferences.computer.org/cloud/2021/)

We show that one framework can efficiently support multiple abstractions. We provide three abstractions of Block, Windowed, and Stateful streaming and demonstrate that many application classes can be developed with ease, correctness, and low processing latency.

From the abstract of our paper:

Large websites and distributed systems employ sophisticated analytics to evaluate successes to celebrate and problems to be addressed. As analytics grow, different teams often require different frameworks, with dozens of packages supporting with streaming and batch processing, SQL and no-SQL. Bringing multiple frameworks to bear on a large, changing dataset often create challenges where data transitions—these impedance mismatches can create brittle glue logic and performance problems that consume developer time. We propose Plumb, a meta-framework that can bridge three different abstractions to meet the needs of a large class of applications in a common workflow. Large-block streaming (Block-Streaming) is suitable for single-pass applications that care about the temporal and spatial locality. Windowed-Streaming allows applications to process a group of data and many reductions. Stateful-Streaming enables applications to keep a long-term state and always-on behavior. We show that it is possible to bridge abstractions, with a common, high-level workflow specification, while the system transitions data batch processing and block- and record-level streaming as required. The challenge in bridging abstractions is to minimize latency while allowing applications to select between sequential and parallel operation, while handling out-of-order data delivery, component failures, and providing clear semantics in the face of missing data. We demonstrate these abstractions evaluating a 10-stage workflow of DNS analytics that has been in production use with Plumb for 2 years, comparing to a brittle hand-built system that has run for more than 3 years.

This conference paper is joint work of Abdul Qadeer and  John Heidemann from USC/ISI.

Plumb is open source software and will be available at: https://ant.isi.edu/software/plumb/index.html

Update 2021-09-26: This paper was given a “special paper award” at IEEE Conference on Cloud Computing 2021! Congratulations, Abdul!

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new workshop report “Overcoming Measurement Barriers to Internet Research” (WOMBIR 2021) in ACM CCR

WOMBIR 2021 was the NSF-sponsored Workshop on Overcoming Measurement Barriers to Internet Research. This workshop was hold in two sessions over several days in January and April 2021, chaired by k.c. claffy, David Clark, Fabian Bustamente, John Heidemann, and Mattijs Monjker. The final report includes contributions from Aaron Schulman and Ellen Zegura as well as all the workshop participants.

From the abstract:

In January and April 2021 we held the Workshop on Overcoming Measurement Barriers to Internet Research (WOMBIR) with the goal of understanding challenges in network and security data set collection and sharing. Most workshop attendees provided white papers describing their perspectives, and many participated in short-talks and discussion in two virtual workshops over five days. That discussion produced consensus around several points. First, many aspects of the Internet are characterized by decreasing visibility of important network properties, which is in tension with the Internet’s role as critical infrastructure. We discussed three specific research areas that illustrate this tension: security, Internet access; and mobile networking. We discussed visibility challenges at all layers of the networking stack, and the challenge of gathering data and validating inferences. Important data sets require longitudinal (long-term, ongoing) data collection and sharing, support for which is more challenging for Internet research than other fields. We discussed why a combination of technical and policy methods are necessary to safeguard privacy when using or sharing measurement data. Workshop participant proposed several opportunities to accelerate progress, some of which require coordination across government, industry, and academia.

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new talk “Observing the Global IPv4 Internet: What IP Addresses Show” as an SKC Science and Technology Webinar

John Heidemann gave the talk “Observing the Global IPv4 Internet: What IP Addresses Show” at the SKC Science and Technology Webinar, hosted by Deepankar Medhi (U. Missouri-Kansas City and NSF) on June 18, 2021.  A video of the talk is on YouTube at https://www.youtube.com/watch?v=4A_gFXi2WeY. Slides are available at https://www.isi.edu/~johnh/PAPERS/Heidemann21a.pdf.

From the abstract:Covid and non-Covid network changes in India; part of a talk about measuring the IPv4 Internet.

Since 2014 the ANT lab at USC has been observing the visible IPv4 Internet (currently 5 million networks measured every 11 minutes) to detect network outages. This talk explores how we use this large-scale, active measurement to estimate Internet reliability and understand the effects of real-world events such as hurricanes. We have recently developed new algorithms to identify Covid-19-related Work-from-Home and other Internet shutdowns in this data. Our Internet outage work is joint work of John Heidemann, Lin Quan, Yuri Pradkin, Guillermo Baltra, Xiao Song, and Asma Enayet with contributions from Ryan Bogutz, Dominik Staros, Abdulla Alwabel, and Aqib Nisar.

This project is joint work of a number of people listed in the abstract above, and is supported by NSF 2028279 (MINCEQ) and CNS-2007106 (EIEIO). All data from this paper is available at no cost to researchers.

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the tsuNAME vulnerability in DNS

On 2020-05-06, researchers at SIDN Labs, (the .nl registry), InternetNZ (the .nz registry) , and at the Information Science Institute at the University of Southern California publicly disclosed tsuNAME, a vulnerability in some DNS resolver software that can be weaponized to carry out DDoS attacks against authoritative DNS servers.

TsuNAME is a problem that results from cyclic dependencies in DNS records, where two NS records point at each other. We found that some recursive resolvers would follow this cycle, greatly amplifying an initial queries and stresses the authoritative servers providing those records.

Our technical report describes a tsuNAME related event observed in 2020 at the .nz authoritative servers, when two domains were misconfigured with cyclic dependencies. It caused the total traffic to growth by 50%. In the report, we show how an EU-based ccTLD experienced a 10x traffic growth due to cyclic dependent misconfigurations.

We refer DNS operators and developers to our security advisory that provides recommendations for how to mitigate or detect tsuNAME.

We have also created a tool, CycleHunter, for detecting cyclic dependencies in DNS zones. Following responsible disclosure practices, we provided operators and software vendors time to address the problem first. We are happy that Google public DNS and Cisco OpenDNS both took steps to protect their public resolvers, and that PowerDNS and NLnet have confirmed their current software is not affected.

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congratulations to Xiao Song for receiving a 2021 USC Viterbi award for MS Student Research

Congratulations to Xiao Song for receiving a 2021 USC Viterbi School of Engineering award for Masters Student Research in the Computer Science Department. This award was on the basis of her work observing work-from-home due to Covid-19, as reported in her poster at the NSF PREPARE-VO Workshop and our arXive technical report.

The award was presented at the May 2021 Viterbi Masters Student Awards Ceremony.

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Data Papers Publications

New paper “Auditing for Discrimination in Algorithms Delivering Job Ads” at TheWebConf 2021

We published a new paper “Auditing for Discrimination in Algorithms Delivering Job Ads” by Basileal Imana (University of Southern California), Aleksandra Korolova (University of Southern California) and John Heidemann (University of Southern California/ISI) at TheWebConf 2021 (WWW ’21).

From the abstract:

Skew in the delivery of real-world ads on Facebook (FB) but not LinkedIn (LI).
Comparison of ad delivery using “Reach” (R) and “Conversion” (C) campaign objectives on Facebook. There is skew for both cases but less skew for “Reach”.

Ad platforms such as Facebook, Google and LinkedIn promise value for advertisers through their targeted advertising. However, multiple studies have shown that ad delivery on such platforms can be skewed by gender or race due to hidden algorithmic optimization by the platforms, even when not requested by the advertisers. Building on prior work measuring skew in ad delivery, we develop a new methodology for black-box auditing of algorithms for discrimination in the delivery of job advertisements. Our first contribution is to identify the distinction between skew in ad delivery due to protected categories such as gender or race, from skew due to differences in qualification among people in the targeted audience. This distinction is important in U.S. law, where ads may be targeted based on qualifications, but not on protected categories. Second, we develop an auditing methodology that distinguishes between skew explainable by differences in qualifications from other factors, such as the ad platform’s optimization for engagement or training its algorithms on biased data. Our method controls for job qualification by comparing ad delivery of two concurrent ads for similar jobs, but for a pair of companies with different de facto gender distributions of employees. We describe the careful statistical tests that establish evidence of non-qualification skew in the results. Third, we apply our proposed methodology to two prominent targeted advertising platforms for job ads: Facebook and LinkedIn. We confirm skew by gender in ad delivery on Facebook, and show that it cannot be justified by differences in qualifications. We fail to find skew in ad delivery on LinkedIn. Finally, we suggest improvements to ad platform practices that could make external auditing of their algorithms in the public interest more feasible and accurate.

This paper was awarded runner-up for best student paper at The Web Conference 2021.

The data from this paper is upon request, please see our dataset page.

This work was reported in the popular press: The Intercept, MIT Technology Review, Wall Street Journal, The Register, VentureBeat, Reuters, The Verge, Engadget, Associated Press.