A Hyper-Converged Video Management System Based on Object Storage
Qiang Wang, She Bo, Qin Zun ying, Li guo dong, Fan Dong · 2020
In recent years, online education, video surveillance industry and live streaming platform have encountered huge evolution. Massive video capture, storage, and access are facing increasing demands. But in large-scale video scenario, traditional video management systems encounter the challenges of reliability, extendibility and fault-tolerance. In this paper, a novel hyper-converged massive video management system based on object storage is designed and realized. The hyper-converged system composes distributed video capture, tiering video storage and read/write splitting load balance. In this loosely coupled system, all components are distributed on each node and communicate via message queues. The components can harness unused resources for example CPU, memory and network of hyper-converged nodes. This system exploits high extensibility and reliability of distributed system to satisfy large-scale video capture, storage and access.