End-Edge-Cloud Collaborative Computing for Deep Learning: A Comprehensive Survey
Beijing Institute of Technology · University of Chinese Academy of Sciences · +2 more institutions
Abstract
The booming development of deep learning applications and services heavily relies on large deep learning models and massive data in the cloud. However, cloud-based deep learning encounters challenges in meeting the application requirements of responsiveness, adaptability, and reliability. Edge-based and end-based deep learning enables rapid, near real-time analysis and response, but edge nodes and end devices usually have limited resources to support large models. This necessitates the integration of end, edge, and cloud computing technologies to combine their different advantages. Despite the existence of numerous studies on edge-cloud collaboration, a comprehensive survey for end-edge-cloud computing-enabled…
Citation impact
- FWCI
- 116.15
- Percentile
- 100%
- References
- 256
Authors
5Topics & keywords
- Cloud computing
- Computer science
- Enhanced Data Rates for GSM Evolution
- Deep learning
- Data science
- Artificial intelligence
- Operating system