EdgeServe: A Streaming System for Decentralized Model Serving
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929253480660992 |
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| author | Shaowang, Ted Krishnan, Sanjay |
| author_facet | Shaowang, Ted Krishnan, Sanjay |
| contents | The relevant features for a machine learning task may arrive as one or more continuous streams of data. Serving machine learning models over streams of data creates a number of interesting systems challenges in managing data routing, time-synchronization, and rate control. This paper presents EdgeServe, a distributed streaming system that can serve predictions from machine learning models in real time. We evaluate EdgeServe on three streaming prediction tasks: (1) human activity recognition, (2) autonomous driving, and (3) network intrusion detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_08028 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | EdgeServe: A Streaming System for Decentralized Model Serving Shaowang, Ted Krishnan, Sanjay Databases Distributed, Parallel, and Cluster Computing Machine Learning The relevant features for a machine learning task may arrive as one or more continuous streams of data. Serving machine learning models over streams of data creates a number of interesting systems challenges in managing data routing, time-synchronization, and rate control. This paper presents EdgeServe, a distributed streaming system that can serve predictions from machine learning models in real time. We evaluate EdgeServe on three streaming prediction tasks: (1) human activity recognition, (2) autonomous driving, and (3) network intrusion detection. |
| title | EdgeServe: A Streaming System for Decentralized Model Serving |
| topic | Databases Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2303.08028 |