Xatu: Richer Neural Network Based Prediction for Video Streaming
Xatu: Richer Neural Network Based Prediction for Video Streaming
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DOI：
10.1145/3489048.3522641
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发表时间：
2022-06
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通讯作者：
Yun Seong Nam;Jianfei Gao;Chandan Bothra;Ehab Ghabashneh;Sanjay G. Rao;Bruno Ribeiro;Jibin Zhan;Hui Zhang
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作者：
Yun Seong Nam;Jianfei Gao;Chandan Bothra;Ehab Ghabashneh;Sanjay G. Rao;Bruno Ribeiro;Jibin Zhan;Hui Zhang
视频流的自适应比特量（ABR）算法的性能取决于准确预测视频块的下载时间。现有的预测方法（i）假设块下载时间由网络吞吐量主导； （ii）Apriori群集会话（例如，基于ISP和CDN），仅从同一集群中的会话中学习。我们做出三项贡献。首先，通过分析现实世界视频流媒体会话的数据，我们表明（i）apriori聚类可防止从相关群集中学习； （ii）诸如第一字节（TTFB）之类的因素是块下载时间的关键组成部分，但不容易将其纳入现有的预测方法中。其次，我们提出了XATU，这是一种新的预测方法，该方法共同学习具有可解释的自动会话聚类方法的神经网络序列模型。 Xatu学习在其认为相关的所有会话中学习聚类规则，并模拟具有多个块依赖性特征（例如TTFB）而不仅仅是吞吐量的序列。第三，使用上述数据集的评估和仿真实验表明，相对于CS2P（一种最先进的预测指标），XATU显着提高了预测准确性23.8％。我们显示，XATU与多种ABR算法（包括MPC（研究良好的ABR算法））和相对于其默认预测变量（CS2P和一个完全连接的神经网络）的多种ABR算法（包括MPC（研究良好的ABR算法）和FUGUABR（最近使用随机控制的算法）集成而提供了可观的性能优势。此外，XATU与MPC结合的表现优于基于深度强化学习的ABR。
The performance of Adaptive Bitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput; and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters; and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.