Livesmart: A QoS-Guaranteed Cost-Minimum Framework of Viewer Scheduling for Crowdsourced Live Streaming

Viewer scheduling among different CDN providers in crowdsourced live streaming (CLS) service is especially challenging due to the large-scale dynamic viewers as well as the time-variant performance of the content delivery network. A practical scheduling method should tackle the following challenges: 1) accurate modeling of viewer patterns and CDN performance; 2) intelligent workload offloading to save costs while guaranteeing the quality of service (QoS); 3) and ease of integration with practical CDN infrastructure in CLS platforms. In this paper, we propose Livesmart, a novel framework that facilitates a QoS-guaranteed cost-efficient approach for CLS services. Specifically, we address the first challenge by carefully designing deep neural networks which make Livestream capture the environment dynamics without any presumptions; we then tackle the second challenge by leveraging the Model Predictive Control (MPC) method which enables Livesmart to make decisions in a long-term way. For the last challenge, we propose a probability shift model based on the realistic CLS delivery structure, thus empowering Livesmart to be practically deployed. We collect real-world data in cooperation with Kuaishou, one of the largest CLS provider in China, and evaluate Livesmart with trace-driven experiments. In comparison with prevalent methods, Livesmart can significantly reduce the CDN bandwidth costs (24.97%-63.45%) and improve the average QoS (5.79%-7.63%).