Learning Based Deadline Aware Congestion Control

Rongji Liao, Jinyao Yan, Tao Lin, Yuhao Chen
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引用次数: 1

Abstract

A large variety of applications are deadline aware such as 360 degree VR (Virtual Reality), video conference and online gaming. In this paper, we propose a congestion control algorithm using deep reinforcement learning named DRL-CC to make the data arrive before the deadline. The initial experiments validate that our method dramatically increases the number of packets arrived before the deadline compared with existing algorithms.
基于学习的截止时间感知拥塞控制
各种各样的应用程序都有截止日期意识,比如360度虚拟现实(VR)、视频会议和在线游戏。在本文中,我们提出了一种使用深度强化学习的拥塞控制算法,称为DRL-CC,以使数据在截止日期之前到达。初步实验证明,与现有算法相比,我们的方法显著增加了在截止日期前到达的数据包数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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