一句话：提出并公开 vbench，首个针对云视频服务/视频即服务负载的基准测试，含语料选择、指标与基线。
论文针对云视频服务这一新兴负载缺乏代表性基准的问题，提出公开基准 vbench。其视频通过算法从百万级商业视频语料中选取，并配有精心构造的指标与基线，以刻画速度、质量和压缩之间的权衡。作者通过 cache、分支和 SIMD 的微架构研究说明视频选择的重要性，并发现商业语料中的趋势不同于其他视频语料。GPU 评测显示上下文至关重要：适合直播，点播将成本从计算转移到存储与网络；对热门视频因需要高压缩高质量副本而不可行，当前软件编码器演进路线反而更合适。
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.
@inproceedings{paperbot4278,
title = {vbench},
author = {Andrea Lottarini and Alex Ramirez and Joel Coburn and Martha A. Kim and Parthasarathy Ranganathan and Daniel Stodolsky and Mark Wachsler},
booktitle = {ASPLOS},
year = {2018},
doi = {10.1145/3173162.3173207}
}