vbench: Benchmarking Video Transcoding in the Cloud
vbench: Benchmarking Video Transcoding in the Cloud
复制标题
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:
影响因子:
--
通讯作者:
Mark Wachsler
中科院分区:
文献类型:
--
作者:
A. Lottarini;Alex Ramírez;Joel Coburn;Martha A. Kim;Parthasarathy Ranganathan;Daniel Stodolsky;Mark Wachsler
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