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1 Ergebnisse
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Improving per-flow fairness by ML-based estimation of compe..:
, In:
2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN)
,
Maeta, Keito
;
Kitagata, Gen
;
Hasegawa, Go
- p. 376-381 , 2022
Link:
https://doi.org/10.1109/ICUFN55119.2022.9829701
RT T1
2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN)
: T1
Improving per-flow fairness by ML-based estimation of competing flows' congestion control algorithm
UL https://suche.suub.uni-bremen.de/peid=ieee-9829701&Exemplar=1&LAN=DE A1 Maeta, Keito A1 Kitagata, Gen A1 Hasegawa, Go YR 2022 SN 2165-8536 K1 Machine learning algorithms K1 Protocols K1 Estimation K1 Bandwidth K1 Internet K1 Indexes K1 Congestion Control K1 Transmission Control Protocol (TCP) K1 Fairness K1 Machine Learning SP 376 OP 381 LK http://dx.doi.org/https://doi.org/10.1109/ICUFN55119.2022.9829701 DO https://doi.org/10.1109/ICUFN55119.2022.9829701 SF ELIB - SuUB Bremen
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