Practical Cloud-Edge Scheduling for Large-Scale Crowdsourced Live Streaming

Rui-Xiao Zhang, Changpeng Yang, Xiaochan Wang, Tianchi Huang, Chenglei Wu, Jiangchuan Liu, Lifeng Sun

Abstract: Even though conventional wisdom claims that in order to improve viewer engagement, the cloud-edge providers should serve the viewers with the nearest edge nodes, however, we show that doing this for crowdsourced live streaming (CLS) services can introduce significant costs inefficiency. In this paper, we first carry out large-scale measurement analysis by using the real-world service data from Huawei Cloud, a representative cloud-edge provider in China. We observe that the massive number of channels has proposed great burdens to the operating expenditure of the cloud-edge providers, and most importantly, unbalanced viewer distribution makes the edge nodes suffer significant costs inefficiency. To tackle the above concerns, we propose AggCast, a novel CLS scheduling framework to optimize the edge node utilization for the cloud-edge provider. The core idea of AggCast is to aggregate some viewers that are initially scattered on different regions, and assign them to fewer pre-selected nodes, thereby reducing bandwidth costs. In particular, by integrating the useful insights obtained from our large-scale measurement, AggCast can not only ensure that quality of experience (QoS) does not suffer degradation, but also satisfy the systematic requirements of CLS services. AggCast has been A/B tested and fully deployed. The online and trace-driven experiments show that, compared to the most prevalent method, AggCast saves over 16.3% back-to-source (BTS) bandwidth costs while significantly improving QoS (startup latency, stall frequency and stall time are reduced over 12.3%, 4.57% and 3.91%, respectively).