Mahimahi: Accurate Record-and-Replay for HTTP

By Ravi Netravali, Anirudh Sivaraman, Somak R. Das, Ameesh Goyal, Keith Winstein, James W. Mickens, Hari Balakrishnan. USENIX Annual Technical Conference, 2015

466 citations recorded by the source corpus.

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Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics (2020, methodology): “The camera and server were connected over a variety of live (LTE and WiFi) and emulated networks via Mahimahi [55].”
PCC Vivace: Online-Learning Congestion Control (2018, methodology): “We use Mahimahi [26] to replay the Verizon-LTE trace provided by [32].”
Learning in situ: a randomized experiment in video streaming (2019, methodology): “tigate this further, we constructed an emulation environment similar to that used in [15]. This involved running the Puffer media server locally, and launching headless Chrome clients inside mahimahi [20] shells to connect to the server. Each mahimahi shell imposed a 40 ms end-to-end delay on traffi”
Neural Adaptive Video Streaming with Pensieve (2017, methodology): “We reformatted throughput traces from both datasets to be compatible with the Mahimahi [32] network emulation tool.”
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP (2019, background): “Other evaluations of QUIC have focused on generic traffic patterns such as regular Web sites [175], [176] without providing”
Copa: Practical Delay-Based Congestion Control for the Internet (2018, methodology): “The slope of the line is ± ˆ δ packets per RTT. Mahimahi [31] emulated link.”