Resource scheduling and management of multidimensional data stream cloud platforms based on deep neural networks
Abstract
We propose a deep learning-based scheduling framework for cloud platforms handling multidimensional data streams. Our model combines temporal modelling, graph-aware reasoning, and entropy regularisation to achieve both rapid convergence and robust task assignment. Experiments on Google, Alibaba, and Microsoft datasets show that our method significantly reduces response time and queue length while improving task completion rate and concurrent throughput. Compared to SVM, RF, and MSCNet, our approach demonstrates superior performance across system-level and robustness metrics, validating its practical deployment potential.
Fields of Study & Subjects
Cloud Computing and Resource Management
Distributed and Parallel Computing Systems
Advanced Database Systems and Queries
Publication details
- Journal / source
- Cyber-Physical Systems
- Volume
- 12
- Issue
- 4
- Pages
- 423–442
- Publication date
- 27 January 2026
- Language
- EN