Software Defined Networking (SDN) is a promising paradigm to provide centralized traffic control. Multimedia traffic control based on SDN is crucial but challenging for Quality of Experience (QoE) optimization. It is very difficult to model and control multimedia traffic because solutions mainly depend on an understanding of the network environment, which is complicated and dynamic. Inspired by the recent advances in artificial intelligence (AI) technologies, we study the adaptive multimedia traffic control mechanism leveraging Deep Reinforcement Learning (DRL). This paradigm combines deep learning with reinforcement learning, which learns solely from rewards by trial-and-error. Results demonstrate that the proposed mechanism is able to control multimedia traffic directly from experience without referring to a mathematical model.

中文翻译：

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软件定义网络中多媒体流量控制的深度强化学习

软件定义网络（SDN）是一种有前途的范例，可以提供集中的流量控制。基于SDN的多媒体流量控制至关重要，但对于体验质量（QoE）优化而言却具有挑战性。建模和控制多媒体流量非常困难，因为解决方案主要取决于对网络环境的理解，这是复杂且动态的。受人工智能（AI）技术最新进展的启发，我们研究了利用深度强化学习（DRL）的自适应多媒体流量控制机制。这种范例将深度学习与强化学习相结合，强化学习仅从反复试验的奖励中学习。结果表明，所提出的机制能够直接根据经验控制多媒体流量，而无需参考数学模型。