VideoJam: Self-Balancing Architecture for Live Video Analytics

Edge-based live video analytics are a promising approach to reduce bandwidth overheads caused by the transmission of raw video streams to the cloud. However, the limited resources available on edge devices make it challenging to successfully process video streams in real-time. This gets further exacerbated when attempting to process video streams from mobile cameras. While mobile cameras are a desirable source of information, thanks to them being in the right place at the right time, they are inherently dynamic and unpredictable. To address these challenges, we propose VideoJam, a decentralized load balancing solution for live video analytics. VideoJam uses a set of load balancers to balance incoming video traffic across replicas without the need of centralized coordination. Exploiting the inherent load dynamicity generated by different video sources, VideoJam predicts the incoming load for each processing component and offloads excessive traffic to less-loaded neighbors. Further, VideoJam operates independently of deployed configurations and cameras present in the system, dynamically adapting to handle load changes and balance video traffic across available resources. Our evaluation shows that VideoJam can adapt to different mixes of mobile and fixed cameras, as well as quickly adapting to configuration changes occurring at runtime. Compared to state-of-the-art solutions, VideoJam achieves 2.91× lower response time, while reducing video data loss by more than 4.64× and generating lower bandwidth overheads.