USENIX ATC '24 and OSDI '24

Ziwen Ye, Peng Cheng Laboratory and Tsinghua Shenzhen International Graduate School; Qing Li, Peng Cheng Laboratory; Chunyu Qiao, ByteDance; Xiaoteng Ma, Tsinghua Shenzhen International Graduate School; Yong Jiang, Peng Cheng Laboratory and Tsinghua Shenzhen International Graduate School; Qian Ma and Shengbin Meng, ByteDance; Zhenhui Yuan, University of Warwick; Zili Meng, HKUST

Video Feed Streaming (e.g., TikTok, Reels) is increasingly popular nowadays. Users will be scheduled to the distribution infrastructure, including content distribution network (CDN) and multi-access edge computing (MEC) nodes, to access the content. Our observation is that the existing proactive content push algorithms, which are primarily based on historical access information and designed for on-demand videos, no longer meet the demands of video feed streaming. The main reason is that video feed streaming applications always push recently generated videos to attract users’ interests, thus lacking historical information when pushing. In this case, push mismatches and load imbalances will be observed, resulting in degraded bandwidth cost and user experience. To this end, we propose KEPC-Push, a Knowledge-Enhanced Proactive Content Push strategy with the \textit{knowledge} of video content features. KEPC-Push employs knowledge graphs to determine the popularity correlation among similar videos (with similar authors, contents, length, etc.) and pushes content based on this guidance. Besides, KEPC-Push designs a hierarchical algorithm to optimize the resource allocation in edge nodes with heterogeneous capabilities and runs at the regional level to shorten the communication distance. Trace-driven simulations show that KEPC-Push saves the peak-period CDN bandwidth costs by 20% and improves the average download speeds by 7% against the state-of-the-art solutions.

https://www.usenix.org/conference/atc24/presentation/ye-ziwen