AggCast: Practical Cost-effective Scheduling for Large-scale Cloud-edge Crowdsourced Live Streaming

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

Abstract: 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. We observe that the massive number of channels has greatly burdened 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 who are initially scattered on different regions, and assign them to fewer pre-selected nodes, thereby reducing bandwidth costs. In particular, by leveraging the insights obtained from our large-scale measurement, AggCast can not only ensure quality of experience (QoS), but also satisfy the systematic requirements of CLS services. AggCast has been A/B tested and fully deployed in a top cloud-edge provider in China for over eight months. The online and trace-driven experiments show that, compared to the common practice, AggCast can save over 15% back-to-source (BTS) bandwidth costs while having no negative impacts on QoS.