Scalable Video Transcoding in Public Clouds

Abstract

In this paper, we present the challenges involved in large-scale video transcoding application in public clouds. We introduce the architecture of an existing video transcoding system which is tightly coupled with an existing video sharing service. We examine the horizontal scalability of the video transcoding system on AWS EC2. With an online transaction processing (OLTP) model, the system achieves linear horizontal scalability up to 1,000 vCPU cores, but starts to experience performance degradation beyond that. We analyze the resource consumption pattern of the existing system, then introduce an improved architecture by adding a message queue layer. This effectively decouples the video transcoding system from the video sharing service and converts the OLTP model into a batch processing model. Large-scale evaluations on AWS EC2 indicate that the improved design maintains linear horizontal scalability at 10,100 vCPU cores. The hybrid design of the system allows it to be easily adapted for other batch processing use cases without the need to modify or recompile the application.