用原始包同时预测多个实时通信质量指标
Modelling Concurrent RTP Flows for End-to-end Predictions of QoS in Real Time Communications
- 基于原始数据的Transformer模型，可处理任意数量并发流
- 单次预测四个关键质量指标，准确率优于现有方法
- 适合网络监控与实时通信优化场景
基于实时传输协议（RTP）的实时通信（RTC）应用，如视频会议，在近年迅速普及。为优化性能，对服务质量（QoS）指标的预测成为关键任务，有助于网络监控与主动调控。然而，现有方法仅针对单个RTP流，难以捕捉流间关系且效率较低。为此，我们提出P2P框架，利用原始数据同时处理多个并发RTP流，并端到端预测多个QoS指标。该框架采用无长度限制的Transformer结构，结合跨流与邻近注意力机制，可处理任意数量流；并采用多任务学习，一次性预测四个核心指标。实验基于真实视频通话采集的大量流量数据，结果表明，P2P在预测精度和时间效率上均显著优于对比模型。
原文摘要 · Abstract (English)
The Real-time Transport Protocol (RTP)-based real-time communications (RTC) applications, exemplified by video conferencing, have experienced an unparalleled surge in popularity and development in recent years. In pursuit of optimizing their performance, the prediction of Quality of Service (QoS) metrics emerges as a pivotal endeavor, bolstering network monitoring and proactive solutions. However, contemporary approaches are confined to individual RTP flows and metrics, falling short in relationship capture and computational efficiency. To this end, we propose Packet-to-Prediction (P2P), a novel deep learning (DL) framework that hinges on raw packets to simultaneously process concurrent RTP flows and perform end-to-end prediction of multiple QoS metrics. Specifically, we implement a streamlined architecture, namely length-free Transformer with cross and neighbourhood attention, capable of handling an unlimited number of RTP flows, and employ a multi-task learning paradigm to forecast four key metrics in a single shot. Our work is based on extensive traffic collected during real video calls, and conclusively, P2P excels comparative models in both prediction performance and temporal efficiency.
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