| Autor: |
Daoyun HU, Jin QI, Qianchun LU, Feng LI, Hongqiang FANG |
| Jazyk: |
čínština |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Dianxin kexue, Vol 37, Pp 107-114 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1000-0801 |
| DOI: |
10.11959/j.issn.1000-0801.2021027 |
| Popis: |
With the development and application of 5G network, the amount of traffic in network increased rapidly, which caused the lack of bandwidth resource.In order to improve the utilization of network resource and satisfy the critical user requirement for QoS (quality of service), a novel traffic engineering algorithm based on deep learning in SDN was proposed.At last, simulation results show that the proposed algorithm not only can calculate an efficient path for service in real time, but also can improve the QoS and the utilization of network resource, as well as reduce network congestion. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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