You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks
With increasing numbers of computer vision and object detection application scenarios, those requiring ultra-low service latency times have become increasingly prominent; e.g., those for autonomous and connected vehicles or smart city applications. The incorporation of machine learning through the a...
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/5/2177 |
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doaj-8ae8c4ed1c57445fa7db9dbbb14c01e12021-03-03T00:02:13ZengMDPI AGApplied Sciences2076-34172021-03-01112177217710.3390/app11052177You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized NetworksZuo Xiang0Patrick Seeling1Frank H. P. Fitzek2Centre for Tactile Internet with Human-in-the-Loop, Technische Universität Dresden, 01187 Dresden, GermanyDepartment of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USACentre for Tactile Internet with Human-in-the-Loop, Technische Universität Dresden, 01187 Dresden, GermanyWith increasing numbers of computer vision and object detection application scenarios, those requiring ultra-low service latency times have become increasingly prominent; e.g., those for autonomous and connected vehicles or smart city applications. The incorporation of machine learning through the applications of trained models in these scenarios can pose a computational challenge. The softwarization of networks provides opportunities to incorporate computing into the network, increasing flexibility by distributing workloads through offloading from client and edge nodes over in-network nodes to servers. In this article, we present an example for splitting the inference component of the YOLOv2 trained machine learning model between client, network, and service side processing to reduce the overall service latency. Assuming a client has 20% of the server computational resources, we observe a more than 12-fold reduction of service latency when incorporating our service split compared to on-client processing and and an increase in speed of more than 25% compared to performing everything on the server. Our approach is not only applicable to object detection, but can also be applied in a broad variety of machine learning-based applications and services.https://www.mdpi.com/2076-3417/11/5/2177object detectionlatency optimizationmobile edge cloudconnected autonomous carssmart cityvideo surveillance |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Zuo Xiang Patrick Seeling Frank H. P. Fitzek |
| spellingShingle |
Zuo Xiang Patrick Seeling Frank H. P. Fitzek You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks Applied Sciences object detection latency optimization mobile edge cloud connected autonomous cars smart city video surveillance |
| author_facet |
Zuo Xiang Patrick Seeling Frank H. P. Fitzek |
| author_sort |
Zuo Xiang |
| title |
You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks |
| title_short |
You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks |
| title_full |
You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks |
| title_fullStr |
You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks |
| title_full_unstemmed |
You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks |
| title_sort |
you only look once, but compute twice: service function chaining for low-latency object detection in softwarized networks |
| publisher |
MDPI AG |
| series |
Applied Sciences |
| issn |
2076-3417 |
| publishDate |
2021-03-01 |
| description |
With increasing numbers of computer vision and object detection application scenarios, those requiring ultra-low service latency times have become increasingly prominent; e.g., those for autonomous and connected vehicles or smart city applications. The incorporation of machine learning through the applications of trained models in these scenarios can pose a computational challenge. The softwarization of networks provides opportunities to incorporate computing into the network, increasing flexibility by distributing workloads through offloading from client and edge nodes over in-network nodes to servers. In this article, we present an example for splitting the inference component of the YOLOv2 trained machine learning model between client, network, and service side processing to reduce the overall service latency. Assuming a client has 20% of the server computational resources, we observe a more than 12-fold reduction of service latency when incorporating our service split compared to on-client processing and and an increase in speed of more than 25% compared to performing everything on the server. Our approach is not only applicable to object detection, but can also be applied in a broad variety of machine learning-based applications and services. |
| topic |
object detection latency optimization mobile edge cloud connected autonomous cars smart city video surveillance |
| url |
https://www.mdpi.com/2076-3417/11/5/2177 |
| work_keys_str_mv |
AT zuoxiang youonlylookoncebutcomputetwiceservicefunctionchainingforlowlatencyobjectdetectioninsoftwarizednetworks AT patrickseeling youonlylookoncebutcomputetwiceservicefunctionchainingforlowlatencyobjectdetectioninsoftwarizednetworks AT frankhpfitzek youonlylookoncebutcomputetwiceservicefunctionchainingforlowlatencyobjectdetectioninsoftwarizednetworks |
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