Ranysha Ware

I am a Postdoctoral Teaching Fellow in CMU’s Computer Science Department.

I did my PhD at CMU also in CSD where I was co-advised by Professor Justine Sherry and Professor Srinivasan Seshan. My thesis was about developing a new methodology for determining if a new congestion control is safe to deploy in the Internet today.

I earned my M.S. in Computer Science from University of Massachusetts Amherst where I worked with Professor Charles Weems in the Architecture and Language Implementation Group. I earned my B.S. in Computer Science from The State University of New York at New Paltz. Prior to CMU, I was Associate Technical Staff at MIT Lincoln Laboratory in the Cyber Analytics and Decision Systems Group.

I was a Facebook Emerging Scholar. I am also a two-time receipient of the National GEM Consortium Fellowship and was named one of SUNY New Paltz’s 40 under Forty Alumni in 2017.

Here is my CV.

news

Aug 2024

I am teaching 15-110 at CMU for Fall 2024: 15-110: Principles of Computing.

Jul 2024

Our paper Reverse-Engineering Congestion Control Algorithm Behavior was accepted to IMC 2024.

May 2024

Our papers “CCAnalyzer: An Efficient and Nearly-Passive Congestion Control Classifier” and “Prudentia: Findings of an Internet Fairness Watchdog” were accepted to SIGCOMM 2024.

Apr 2024

I accepted a position as a Mark Stehlik Postdoctoral Teaching Fellow in the Computer Science Department at CMU starting Fall 2024.

Aug 2023

I taught a summer course at CMU for 6 weeks: 15-112: Fundamentals of Programming and Computer Science.

publications

Reverse-Engineering Congestion Control Algorithm Behavior Ferreira, Margarida, Narayan, Akshay, Ware, Ranysha, Kothari, Yash, Lynce, Ines, Martins, Ruben, and Sherry, Justine In Proceedings of the IMC 2024 Conference 2024

CCAnalyzer: An Efficient and Nearly-Passive Congestion Control Classifier Ware, Ranysha, Philip, Adithya Abraham, Hungria, Nicholas, Kothari, Yash, Sherry, Justine, and Seshan, Srinivasan In Proceedings of the ACM SIGCOMM 2024 Conference 2024 [PDF]

Prudentia: Findings of an Internet Fairness Watchdog Philip, Adithya Abraham, Athapathu, Rukshani, Ware, Ranysha, Mkocheko, Fabian Francis, Schlomer, Alexis, Shou, Mengrou, Meng, Zili, Seshan, Srinivasan, and Sherry, Justine In Proceedings of the ACM SIGCOMM 2024 Conference 2024 [PDF]

Revisiting TCP Congestion Control Throughput Models & Fairness Properties at Scale Philip, Adithya Abraham, Ware, Ranysha, Athapathu, Rukshani, Sherry, Justine, and Sekar, Vyas In Proceedings of the 21st ACM Internet Measurement Conference 2021 [Abstract] [PDF]

Much of our understanding of congestion control algorithm (CCA) throughput and fairness is derived from models and measurements that (implicitly) assume congestion occurs in the last mile. That is, these studies evaluated CCAs in "small scale" edge settings at the scale of tens of flows and up to a few hundred Mbps bandwidths. However, recent measurements show that congestion can also occur at the core of the Internet on inter-provider links, where thousands of flows share high bandwidth links. Hence, a natural question is: Does our understanding of CCA throughput and fairness continue to hold at the scale found in the core of the Internet, with 1000s of flows and Gbps bandwidths? Our preliminary experimental study finds that some expectations derived in the edge setting do not hold at scale. For example, using loss rate as a parameter to the Mathis model to estimate TCP NewReno throughput works well in edge settings, but does not provide accurate throughput estimates when thousands of flows compete at high bandwidths. In addition, BBR - which achieves good fairness at the edge when competing solely with other BBR flows - can become very unfair to other BBR flows at the scale of the core of the Internet. In this paper, we discuss these results and others, as well as key implications for future CCA analysis and evaluation.

Beyond Jain’s Fairness Index: Setting the Bar For The Deployment of Congestion Control Algorithms Ware, Ranysha, Mukerjee, Matthew K., Seshan, Srinivasan, and Sherry, Justine In Proceedings of the 18th ACM Workshop on Hot Topics in Networks 2019 [Abstract] [PDF] [Slides] [Video]

The Internet community faces an explosion in new congestion control algorithms such as Copa, Sprout, PCC, and BBR. In this paper, we discuss considerations for deploying new algorithms on the Internet. While past efforts have focused on achieving ‘fairness’ or ‘friendliness’ between new algorithms and deployed algorithms, we instead advocate for an approach centered on quantifying and limiting harm caused by the new algorithm on the status quo. We argue that a harm-based approach is more practical, more future proof, and handles a wider range of quality metrics than traditional notions of fairness and friendliness.

Modeling BBR’s Interactions with Loss-Based Congestion Control Ware, Ranysha, Mukerjee, Matthew K., Seshan, Srinivasan, and Sherry, Justine In Proceedings of the Internet Measurement Conference 2019 [Abstract] [PDF] [Slides] [Video]

BBR is a new congestion control algorithm (CCA) deployed for Chromium QUIC and the Linux kernel. As the default CCA for YouTube (which commands 11+% of Internet traffic), BBR has rapidly become a major player in Internet congestion control. BBR’s fairness or friendliness to other connections has recently come under scrutiny as measurements from multiple research groups have shown undesirable outcomes when BBR competes with traditional CCAs. One such outcome is a fixed, 40% proportion of link capacity consumed by a single BBR flow when competing with as many as 16 loss-based algorithms like Cubic or Reno. In this short paper, we provide the first model capturing BBR’s behavior in competition with loss-based CCAs. Our model is coupled with practical experiments to validate its implications. The key lesson is this: under competition, BBR becomes window-limited by its ‘inflight cap’ which then determines BBR’s bandwidth consumption. By modeling the value of BBR’s in-flight cap under varying network conditions, we can predict BBR’s throughput when competing against Cubic flows with a median error of 5%, and against Reno with amedian of 8%.