Forecasting TCP's Rate to Speed up Slow Start

نویسندگان

چکیده

Selection of the optimal transmission rate in packet-switched best-effort networks is challenging. Typically, senders do not have any information about end-to-end path and should congest connection but at once fully utilize it. The accomplishment these goals lead to congestion control protocols such as TCP Reno, Cubic, or BBR that adapt sending according extensive measurements characteristics by monitoring packets related acknowledgments. To improve speed up this adaptation, we propose evaluate a machine learning approach for prediction rates from metrics provided stack. For neural network trained evaluated. implemented stack slow start. customizable performant implementation extended Berkeley packet filter used extract relevant data kernel space stack, forward user prediction, feed result back Results online experiment show improvement flow completion time 30%.

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ژورنال

عنوان ژورنال: IEEE open journal of the Computer Society

سال: 2022

ISSN: ['2644-1268']

DOI: https://doi.org/10.1109/ojcs.2022.3208701