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Title:

Modeling TCP Performance Using Graph Neural Networks

Document type:
Konferenzbeitrag
Author(s):
Jaeger, Benedikt; Helm, Max; Schwegmann, Lars; Carle, Georg
Abstract:
TCP throughput and RTT prediction are essential to model TCP behavior and optimize network configurations. Flows adapt their sending rate to network parameters like link capacity or buffer size and interact with parallel flows. Especially the elastic behavior of TCP congestion control can vary, even when only slight changes in the network occur. Thus, existing analytical models for TCP behavior reach their limits due to the number and complexity of different algorithms. Machine learning approach...     »
Keywords:
congestion control, graph neural networks, throughput, round-trip time, TCP modeling
Book / Congress title:
Proceedings of the 1st International Workshop on Graph Neural Networking
Publisher:
Association for Computing Machinery
Publisher address:
New York, NY, USA
Year:
2022
Month:
December
Pages:
18–23
Print-ISBN:
9781450399333
Bookseries title:
GNNet ’22
Fulltext / DOI:
doi:10.1145/3565473.3569190
WWW:
https://doi.org/10.1145/3565473.3569190
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