No abstract

This paper presents vbench, a publicly available benchmark for cloud video services. We are the first study, to the best of our knowledge, to characterize the emerging video-as-aservice workload. Unlike prior video processing benchmarks, vbench's videos are algorithmically selected to represent a large commercial corpus of millions of videos. Reflecting the complex infrastructure that processes and hosts these videos, vbench includes carefully constructed metrics and baselines. The combination of validated corpus, baselines, and metrics reveal nuanced tradeoffs between speed, quality, and compression. We demonstrate the importance of video selection with a microarchitectural study of cache, branch, and SIMD behavior. vbench reveals trends from the commercial corpus that are not visible in other video corpuses. Our experiments with GPUs under vbench's scoring scenarios reveal that context is critical: GPUs are well suited for live-streaming, while for video-on-demand shift costs from compute to storage and network. Counterintuitively, they are not viable for popular videos, for which highly compressed, high quality copies are required. We instead find that popular videos are currently well-served by the current trajectory of software encoders.

This paper presents vbench, a publicly available benchmark for cloud video services. We are the first study, to the best of our knowledge, to characterize the emerging video-as-aservice workload. Unlike prior video processing benchmarks, vbench's videos are algorithmically selected to represent a large commercial corpus of millions of videos. Reflecting the complex infrastructure that processes and hosts these videos, vbench includes carefully constructed metrics and baselines. The combination of validated corpus, baselines, and metrics reveal nuanced tradeoffs between speed, quality, and compression. We demonstrate the importance of video selection with a microarchitectural study of cache, branch, and SIMD behavior. vbench reveals trends from the commercial corpus that are not visible in other video corpuses. Our experiments with GPUs under vbench's scoring scenarios reveal that context is critical: GPUs are well suited for live-streaming, while for video-on-demand shift costs from compute to storage and network. Counterintuitively, they are not viable for popular videos, for which highly compressed, high quality copies are required. We instead find that popular videos are currently well-served by the current trajectory of software encoders.

Video processing is foundational to a spectrum of important workloads: video sharing, video conferencing, photos/video archival, virtual/augmented reality, cloud gaming, and live streaming. The exponential growth in these workloads, coincident with a slowing Moore's law, poses significant challenges to deliver more computing at higher efficiencies. In this article, we present the design of a new accelerator-the video coding unit-targeted at warehouse-scale (cloud) video transcoding. Our deployment at scale in Google, the first of its kind, has demonstrated significant benefits on several products (YouTube, Meet, Stadia, Photos, etc.) serving billions of users.