Mahak: An Automated and Efficient Assessment Framework for Internet Control Algorithms
Parsa Pazhooheshy, Soheil Abbasloo, Yashar Ganjali · 2025
Network protocols often suffer from undetected performance degradations due to inadequate testing and a lack of efficient and accurate evaluation tools across diverse network configurations. Current methodologies face challenges like pre-modeling, oversimplifications, and focusing on limited failure modes, making them costly, impractical, or imprecise.We propose Mahak, a novel black-box framework that reconstructs the empirical performance of a given protocol throughout the multidimensional configuration space using an active learning-guided sampling strategy, without prior modeling or knowledge of the internal algorithm of the protocol. Applied to state-of-the-art Internet Congestion Control (e.g., BBR2, Sage, Orca) and Adaptive Bitrate Streaming protocols (e.g. Pensieve, BOLA, RobustMPC), Mahak explores less than 0.1% of the configuration space, and achieves up to a 12.5× reduction in mapping error compared to interpolation-based methods.By systematically identifying the complete empirical performance surface, rather than focusing on a single prominent failure, Mahak uncovers issues that would otherwise remain hidden. This end-to-end mapping equips protocol designers, QA engineers, and network operators with actionable data-driven insights across diverse metrics and configurations, supporting more reliable deployments.