Name
Abstract | ||
|---|---|---|
A new form of cloud computing, serverless computing, is drawing attention as a new way to design micro-services architectures. In a serverless computing environment, services are developed as service functional units. The function development environment of all serverless computing framework at present is CPU based. In this paper, we propose a GPU-supported serverless computing framework that can deploy services faster than existing serverless computing framework using CPU. Our core approach is to integrate the open source serverless computing framework with NVIDIA-Docker and deploy services based on the GPU support container. We have developed an API that connects the open source framework to the NVIDIA-Docker and commands that enable GPU programming. In our experiments, we measured the performance of the framework in various environments. As a result, developers who want to develop services through the framework can deploy high-performance micro services and developers who want to run deep learning programs without a GPU environment can run code on remote GPUs with little performance degradation. |
Year | Venue | Field |
|---|---|---|
2018 | PDP | Micro services,Development environment,Computer science,General-purpose computing on graphics processing units,Artificial intelligence,Deep learning,Cloud computing,Distributed computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tae Joon Jun | 1 | 8 | 7.58 |
| Daeyoun Kang | 2 | 5 | 1.44 |
| Do-Hyeun Kim | 3 | 88 | 22.95 |
| Daeyoung Kim | 4 | 0 | 0.34 |