Seminar: Graduate Seminar
Buffer isolation with imperfect congestion control classification
| Due to their increasing aggressiveness, recent congestion control algorithms (CCAs) can quickly starve standard TCP flows in their shared router queues. Existing solutions based on fair queueing are not scalable enough, and those based on admission control do not fit all CCAs. Independently, building on the popularization of machine learning, recent papers have designed several CCA classifiers.
In this seminar, we introduce a buffer isolation mode where incoming flows first undergo CCA classification, and then are mapped to distinct CCA-based queues based on their classified CCA. We provide a fundamental analysis for such an isolation mode, and present a simple and an advanced aggressiveness model for its performance. Then, in evaluations, we show how this isolation mode clearly outperforms the existing sharing mode, as well as how our advanced model can accurately represent its performance. We further show how using the advanced model, we can maximize the performance by optimizing the queue service rates, surprisingly obtaining fair CCA shares with imperfect classification. M.Sc. student under the supervision of Prof. Isaac Keslassy.
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