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-a-service 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.
Highlights
Video sharing represents a growing fraction of internet traffic
We show boxplots for the % of time spent on front-end (FE), bad speculation (BAD), waiting for memory (BE/Mem), waiting for the back-end (BE/Core), or retiring instructions (RET)
Our results show that for all sets 15% of the time is spent on front-end stalls, 10% on bad speculation, 15% of the time is spent waiting for memory, with the remaining 60% spent retiring instructions or waiting for functional units
Summary
Video sharing represents a growing fraction of internet traffic. For example, in the November 2016 Facebook earnings. To keep up with growing usage, video on demand providers such as Netflix, YouTube, and Facebook maintain large video serving infrastructures All these services perform a large number of transcoding operations [40], i.e., decoding a compressed video into raw frames and re-encoding it in a new compressed format. To establish some common ground, this paper presents a video transcoding benchmark, vbench, that reflects the transcode demands of a video sharing service such as YouTube. We find that while GPUs today cannot meet the strict quality and compression targets for popular videos, newer and more complex software encoders can These studies demonstrate the relevance of our benchmark and the importance of having a curated set of videos, meaningful baselines, and encoding scenarios to evaluate new transcoding solutions
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