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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Bibliographic Details
Published in:	2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS) pp. 1 - 6
Main Authors:	Zhang, Xiaoping, Wang, Yunlan, Zhao, Tianhai, Hu, Shuai, Zhang, Hui
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22.09.2023
Subjects:	Adaptation models adaptive flow control network flow Concurrent computing high concurrency Machine learning algorithms Q-learning sentinel Stability analysis Throughput Training
Online Access:	Get full text
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Summary:	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 flow control requires manual parameter setting based on thresholds, which not only requires a large number of parameter settings but also affects system availability. This paper proposes an adaptive flow control algorithm based on q-learning, designs and implements a q-learning model for adaptive flow control, and considers the three elements of state, action and reward, where the state includes CPU utilization, average response time, requests per second, and concurrent threads;actions include flow control decisions, that is pass or reject; rewards represent the feedback results of each decision. The algorithm can use the trained q-learning model to select the best flow control decision based on the current state, so as to realize the adaptive adjustment and control of network flow. In the case of high network load, the algorithm will intelligently adjust the flow control strategy to reduce network congestion and delay, improve network performance and reliability, and solve the problem of system instability caused by high throughput under high concurrency.
DOI:	10.1109/DOCS60977.2023.10294952