Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152998
Title: Hysia : serving DNN-based video-to-retail applications in cloud
Authors: Zhang, Huaizheng
Li, Yuanming
Ai, Qiming
Luo, Yong
Wen, Yonggang
Jin, Yichao
Ta, Nguyen Binh Duong
Keywords: Engineering::Computer science and engineering::Information systems
Engineering::Computer science and engineering::Computer applications
Issue Date: 2020
Source: Zhang, H., Li, Y., Ai, Q., Luo, Y., Wen, Y., Jin, Y. & Ta, N. B. D. (2020). Hysia : serving DNN-based video-to-retail applications in cloud. 28th ACM International Conference on Multimedia, 4457-4460. https://dx.doi.org/10.1145/3394171.3414536
Project: NRF2017EWT-EP003- 023
NTU–ACE2020-01
Abstract: Combining \underline{v}ideo streaming and online \underline{r}etailing (V2R) has been a growing trend recently. In this paper, we provide practitioners and researchers in multimedia with a cloud-based platform named Hysia for easy development and deployment of V2R applications. The system consists of: 1) a back-end infrastructure providing optimized V2R related services including data engine, model repository, model serving and content matching; and 2) an application layer which enables rapid V2R application prototyping. Hysia addresses industry and academic needs in large-scale multimedia by: 1) seamlessly integrating state-of-the-art libraries including NVIDIA video SDK, Facebook faiss, and gRPC; 2) efficiently utilizing GPU computation; and 3) allowing developers to bind new models easily to meet the rapidly changing deep learning (DL) techniques. On top of that, we implement an orchestrator for further optimizing DL model serving performance. Hysia has been released as an open source project on GitHub, and attracted considerable attention. We have published Hysia to DockerHub as an official image for seamless integration and deployment in current cloud environments.
URI: https://hdl.handle.net/10356/152998
ISBN: 9781450379885
DOI: 10.1145/3394171.3414536
Rights: © 2020 Association for Computing Machinery. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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