Distributed asynchronous learning for multipath data transmission based on P-DDQN
| Autor: | Yuming Zhang, Chengxiao Yu, Kang Liu, Deyun Gao, Mingyuan Liu, Wei Quan |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer Networks and Communications Computer science Network packet Distributed computing Stability (learning theory) Throughput Scheduling (computing) Asynchronous learning Asynchronous communication Computer Science::Networking and Internet Architecture Electrical and Electronic Engineering Computer Science::Operating Systems Multipath propagation |
| Zdroj: | China Communications. 18:62-74 |
| ISSN: | 1673-5447 |
| Popis: | Adaptive packet scheduling can efficiently enhance the performance of multipath Data Transmission. However, realizing precise packet scheduling is challenging due to the nature of high dynamics and unpredictability of network link states. To this end, this paper proposes a distributed asynchronous deep reinforcement learning framework to intensify the dynamics and prediction of adaptive packet scheduling. Our framework contains two parts: local asynchronous packet scheduling and distributed cooperative control center. In local asynchronous packet scheduling, an asynchronous prioritized replay double deep Q-learning packets scheduling algorithm is proposed for dynamic adaptive packet scheduling learning, which makes a combination of prioritized replay double deep Q-learning network (P-DDQN) to make the fitting analysis. In distributed cooperative control center, a distributed scheduling learning and neural fitting acceleration algorithm to adaptively update neural network parameters of P-DDQN for more precise packet scheduling. Experimental results show that our solution has a better performance than Random weight algorithm and Round-Robin algorithm in throughput and loss ratio. Further, our solution has 1.32 times and 1.54 times better than Random weight algorithm and Round—Robin algorithm on the stability of multipath data transmission, respectively. |
| Databáze: | OpenAIRE |
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