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• W4386365397 abstract "The resource efficiency of video analytics workloads is critical for large-scale deployments on edge nodes and cloud clusters. Recent advanced systems have benefited from techniques including video compression, frame filtering, and deep model acceleration. However, based on our year-long experience of operating a real-time video analytics system on more than 1000 cameras, we identified a previously overlooked bottleneck of end-to-end concurrency: video decoding. To support concurrent video inference at scale, in this work, we investigate a new task, named video packet gating, which selectively filters packets before running a decoder. We propose a novel multi-view embedding approach for video packets and present PacketGame that has both theoretical performance guarantee and practical system designs. Experiments on both public datasets and a real system show PacketGame saves 52.0--79.3% decoding costs and achieves 2.1--4.8× concurrency compared to original workloads. Comparisons with four state-of-the-art complementary methods show the superiority of PacketGame in end-to-end concurrency." @default.
• W4386365397 created "2023-09-02" @default.
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• W4386365397 date "2023-09-01" @default.
• W4386365397 modified "2023-10-17" @default.
• W4386365397 title "PacketGame: Multi-Stream Packet Gating for Concurrent Video Inference at Scale" @default.
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• W4386365397 doi "https://doi.org/10.1145/3603269.3604825" @default.
• W4386365397 hasPublicationYear "2023" @default.
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