Name
Title | ||
|---|---|---|
Value Iteration Architecture based Deep Learning for Intelligent Routing Exploiting Heterogeneous Computing Platforms |
Abstract | ||
|---|---|---|
Recently, the rapid advancement of high computing platforms has accelerated the development and applications of artificial intelligence techniques. Deep learning, which has been regarded as the next paradigm to revolutionize usersu0027 experiences, has attracted networking researchersu0027 interest to relieve the burden due to the exponentially growing traffic and increasing complexities. Various intelligent packet transmission strategies have been proposed to tackle different network problems. However, most of the existing research just focuses on the network related improvements and neglects the analysis about the computation consumptions. In this paper, we propose a Value Iteration Architecture based Deep Learning (VIADL) method to conduct routing design to address the limitations of existing deep learning based routing algorithms in dynamic networks. Besides the network performance analysis, we also study the complexity of our proposal as well as the resource consumptions in different deployment manners. Moreover, we adopt the Heterogeneous Computing Platform (HCP) to conduct the training and running of the proposed VIADL since the theoretical analysis demonstrates the significant reduction of the time complexity with the multiple GPUs in HCPs. Furthermore, simulation results demonstrate that compared with the existing deep learning based method, our proposal can guarantee more stable network performance when network topology changes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tc.2018.2874483 | IEEE Transactions on Computers |
Field | DocType | Citations |
Computer science,Markov decision process,Symmetric multiprocessor system,Network topology,Supervised learning,Real-time computing,Artificial intelligence,Heterogeneous network,Deep learning,Network performance,Distributed computing,Applications of artificial intelligence | Journal | 1 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bomin Mao | 1 | 3 | 1.07 |
| Fengxiao Tang | 2 | 96 | 4.83 |
| Nei Kato | 3 | 3982 | 263.66 |