| Title |
Poster: MLess: Deep Learning Application Platform for Smart Cities |
| ID_Doc |
43732 |
| Authors |
Makino, S; Okoshi, T; Nakazawa, J |
| Title |
Poster: MLess: Deep Learning Application Platform for Smart Cities |
| Year |
2024 |
| Published |
|
| DOI |
10.1145/3643832.3661394 |
| Abstract |
Many smart city applications utilize deep learning technologies to process the data generated by sensors and smart devices. However, current application hosting platforms are not suitable for deep learning applications, because of their special requirements, including GPU and large trained model data. We propose a smart city application hosting platform named MLess, which serves and scales deep-learning applications across servers. MLess adds an extra abstraction layer between applications and executing hosts, thus it allows developers to write applications in a serverless manner. We developed a Proof-of-Concept implementation of MLess and made preliminary evaluations against it. In future work, we plan to add QoS support like inference accuracy or throughput. |
| Author Keywords |
Smart Cities; Serverless Computing; Deep Learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:001258320200065 |
| WoS Category |
Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Telecommunications |
| Research Area |
Computer Science; Telecommunications |
| PDF |
https://dl.acm.org/doi/pdf/10.1145/3643832.3661394
|