vbench: Benchmarking Video Transcoding in the Cloud

vbench: Benchmarking Video Transcoding in the Cloud
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vbench:云端视频转码基准测试

DOI:
10.1145/3173162.3173207
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发表时间:
2018
期刊:
Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems
影响因子:
--
通讯作者:
Mark Wachsler
Mark Wachsler
中科院分区:
--
文献类型:
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作者:
A. Lottarini;Alex Ramírez;Joel Coburn;Martha A. Kim;Parthasarathy Ranganathan;Daniel Stodolsky;Mark Wachsler

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本文介绍了vbench,一个公开可用的云视频服务基准。据我们所知,我们是第一个描述新兴视频即服务工作量的研究。与之前的视频处理基准不同,vbench的视频是通过算法选择来代表数百万个视频的大型商业语料库的。反映处理和托管这些视频的复杂基础设施,vbench包括精心构建的指标和基线。经过验证的语料库、基线和度量的组合揭示了速度、质量和压缩之间的微妙权衡。我们通过对缓存、分支和SIMD行为的微架构研究来证明视频选择的重要性。Vbench揭示了其他视频语料库中不可见的商业语料库的趋势。我们在vbench的评分场景下对gpu的实验表明,上下文是至关重要的:gpu非常适合直播流媒体,而视频点播从计算到存储和网络的转移成本。与直觉相反,它们不适用于流行视频,因为流行视频需要高度压缩、高质量的拷贝。相反,我们发现流行的视频目前很好地服务于软件编码器的当前轨迹。
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.
DOI: 10.1109/tcsvt.2015.2461971
发表时间: 2016-11
影响因子: 8.4
作者:
Felix J. Mercer Moss;Ke Wang;Fan Zhang;R. Baddeley;D. Bull
通讯作者: Felix J. Mercer Moss;Ke Wang;Fan Zhang;R. Baddeley;D. Bull