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Neil Agarwal

dblp:232/0904 · DBLP profile ↗
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5ranked-venue papers
3as first author
4since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 5 · 3 first-author · 4 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
3 papers
Transport protocols and congestion control · 63% Edge and fog computing · 25% Content delivery and video streaming · 12%
Artificial intelligence
1 paper
Efficient and distributed learning · 100%
Network and information security
1 paper
Privacy and data protection · 100%
Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%

Topics — the 11 heaviest of 13, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Transport protocols and congestion control
rate control
0.912025
Mowgli: Passively Learned Rate Control for Real-Time Video · NSDI 2025
Transport protocols and congestion control › real-time communication
real-time video
0.912025
Mowgli: Passively Learned Rate Control for Real-Time Video · NSDI 2025
Machine learning › Efficient and distributed learning
model compression
0.712023
Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge · NSDI 2023
Machine learning › Efficient and distributed learning
model merging
0.712023
Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge · NSDI 2023
Multimedia analysis and retrieval
video content analysis
0.712023
Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics · NSDI 2023
Edge and fog computing
video analytics
0.712023
Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge · NSDI 2023
Privacy and data protection
privacy-preserving query processing
0.612022
Privid: Practical, Privacy-Preserving Video Analytics Queries · NSDI 2022
Privacy and data protection › privacy-preserving computation › privacy-preserving multimedia processing
privacy-preserving video analytics
0.612022
Privid: Practical, Privacy-Preserving Video Analytics Queries · NSDI 2022
Transport protocols and congestion control
delay-based congestion control
0.412020
Mind the delay: the adverse effects of delay-based TCP on HTTP · CoNEXT 2020
Content delivery and video streaming
web performance
0.412020
Mind the delay: the adverse effects of delay-based TCP on HTTP · CoNEXT 2020
Edge and fog computing › video analytics
real-time video analytics
0.212023
Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge · NSDI 2023

Methods — techniques the papers use, named apart from their topics

passive learning · 0.9congestion control detection · 0.4client-side measurement · 0.4
YearPublicationVenuePosition
2025 Mowgli: Passively Learned Rate Control for Real-Time Video
Neil Agarwal, Rui Pan 0003, Francis Y. Yan, Ravi Netravali
NSDI1
2023 Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics
Neil Agarwal, Ravi Netravali
NSDI1
2023 Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge
Arthi Padmanabhan, Neil Agarwal, Anand Padmanabha Iyer, Ganesh Ananthanarayanan, Yuanchao Shu, Nikolaos Karianakis, Guoqing Harry Xu, Ravi Netravali
NSDI2
2022 Privid: Practical, Privacy-Preserving Video Analytics Queries
Frank Cangialosi, Neil Agarwal, Venkat Arun, Junchen Jiang, Srinivas Narayana, Anand D. Sarwate, Ravi Netravali
NSDI2
2020 Mind the delay: the adverse effects of delay-based TCP on HTTP
abstract
The last three decades have seen much evolution in web and network protocols: amongst them, a transition from HTTP/1.1 to HTTP/2 and a shift from loss-based to delay-based TCP congestion control algorithms. This paper argues that these two trends come at odds with one another, ultimately hurting web performance. Using a controlled synthetic study, we show how delay-based congestion control protocols (e.g., BBR and CUBIC + Hybrid Slow Start) result in the underestimation of the available congestion window in mobile networks, and how that dramatically hampers the effectiveness of HTTP/2. To quantify the impact of such finding in the current web, we evolve the web performance toolbox in two ways. First, we develop Igor, a client-side TCP congestion control detection tool that can differentiate between loss-based and delay-based algorithms by focusing on their behavior during slow start. Second, we develop a Chromium patch which allows fine-grained control on the HTTP version to be used per domain. Using these new web performance tools, we analyze over 300 real websites and find that 67% of sites relying solely on delay-based congestion control algorithms have better performance with HTTP/1.1.
Neil Agarwal, Matteo Varvello, Andrius Aucinas, Fabián E. Bustamante, Ravi Netravali
CoNEXT1