TY - GEN
T1 - Live video analytics at scale with approximation and delay-tolerance
AU - Zhang, Haoyu
AU - Ananthanarayanan, Ganesh
AU - Bodik, Peter
AU - Philipose, Matthai
AU - Bahl, Paramvir
AU - Freedman, Michael J.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Video cameras are pervasively deployed for security and smart city scenarios, with millions of them in large cities worldwide. Achieving the potential of these cameras requires efficiently analyzing the live videos in real-time. We describe VideoStorm, a video analytics system that processes thousands of video analytics queries on live video streams over large clusters. Given the high costs of vision processing, resource management is crucial. We consider two key characteristics of video analytics: resource-quality tradeoff with multi-dimensional configurations, and variety in quality and lag goals. VideoStorm’s offline profiler generates query resource-quality profile, while its online scheduler allocates resources to queries to maximize performance on quality and lag, in contrast to the commonly used fair sharing of resources in clusters. Deployment on an Azure cluster of 101 machines shows improvement by as much as 80% in quality of real-world queries and 7× better lag, processing video from operational traffic cameras.
AB - Video cameras are pervasively deployed for security and smart city scenarios, with millions of them in large cities worldwide. Achieving the potential of these cameras requires efficiently analyzing the live videos in real-time. We describe VideoStorm, a video analytics system that processes thousands of video analytics queries on live video streams over large clusters. Given the high costs of vision processing, resource management is crucial. We consider two key characteristics of video analytics: resource-quality tradeoff with multi-dimensional configurations, and variety in quality and lag goals. VideoStorm’s offline profiler generates query resource-quality profile, while its online scheduler allocates resources to queries to maximize performance on quality and lag, in contrast to the commonly used fair sharing of resources in clusters. Deployment on an Azure cluster of 101 machines shows improvement by as much as 80% in quality of real-world queries and 7× better lag, processing video from operational traffic cameras.
UR - http://www.scopus.com/inward/record.url?scp=85076880182&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076880182&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2017
SP - 377
EP - 392
BT - Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2017
PB - USENIX Association
T2 - 14th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2017
Y2 - 27 March 2017 through 29 March 2017
ER -