Name
Title | ||
|---|---|---|
Task Scheduling with Optimized Transmission Time in Collaborative Cloud-Edge Learning |
Abstract | ||
|---|---|---|
Deep learning has been applied in many recent advanced applications in the field of transportation, finance and medicine. These applications require significant computation resources and large-scale training samples. Cloud becomes a natural choice for conducting these learning tasks due to its abundant resources. However, deeper penetration of deep learning techniques in mission critical applications, like driverless car, calls for stricter time requirement to guarantee its interaction and larger amount of dataset for training to guarantee its accuracy, which cannot be easily satisfied by the cloud and makes the network transmission become the bottleneck. Edge learning emerges to be a promising direction to reduce data transmission time by processing and compressing the raw data at the edge of the network, while brings the concern of accuracy reduction at the meantime. To balance this tradeoff under cloud-edge architecture, we study a task scheduling problem for reducing weighted transmission time which takes learning accuracy into consideration. We also propose efficient scheduling algorithms which are able to achieve up to 50% reduction in makespan with extensive trace-driven simulations. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICCCN.2018.8487352 | 2018 27th International Conference on Computer Communication and Networks (ICCCN) |
Keywords | Field | DocType |
optimized transmission time,finance,medicine,significant computation resources,large-scale training samples,natural choice,learning tasks,abundant resources,deeper penetration,deep learning techniques,mission critical applications,driverless car,stricter time requirement,network transmission,data transmission time,raw data,cloud-edge architecture,task scheduling problem,weighted transmission time,collaborative cloud-edge learning,transportation | Bottleneck,Job shop scheduling,Task analysis,Scheduling (computing),Computer science,Artificial intelligence,Deep learning,Mission critical,Transmission time,Distributed computing,Cloud computing | Conference |
ISSN | ISBN | Citations |
1095-2055 | 978-1-5386-5157-5 | 2 |
PageRank | References | Authors |
0.36 | 9 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yutao Huang | 1 | 8 | 1.74 |
| Yifei Zhu | 2 | 39 | 6.83 |
| Xiaoyi Fan | 3 | 52 | 9.84 |
| Xiaoqiang Ma | 4 | 330 | 29.44 |
| Fangxin Wang | 5 | 42 | 6.80 |
| Jiangchuan Liu | 6 | 4340 | 310.86 |
| Ziyi Wang | 7 | 2 | 3.07 |
| Yong Cui | 8 | 906 | 94.36 |