Q-Learning Based Adaptive Flow Control

With the rapid development of the internet and the expansion of its application scale, the volatility and complexity of network flow in highly concurrent environments have become increasingly prominent. Traditional static control strategies have been difficult to meet the actual needs. Traditional f...

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Veröffentlicht in:	2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS) S. 1 - 6
Hauptverfasser:	Zhang, Xiaoping, Wang, Yunlan, Zhao, Tianhai, Hu, Shuai, Zhang, Hui
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 22.09.2023
Schlagworte:	Adaptation models adaptive flow control network flow Concurrent computing high concurrency Machine learning algorithms Q-learning sentinel Stability analysis Throughput Training
Online-Zugang:	Volltext
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