한국인터넷정보학회 논문지
Current Result Document :
| 한글제목(Korean Title) |
A Lightweight Software-Defined Routing Scheme for 5G URLLC in Bottleneck Networks |
| 영문제목(English Title) |
A Lightweight Software-Defined Routing Scheme for 5G URLLC in Bottleneck Networks |
| 저자(Author) |
Sa Math
Prohim Tam
Seokhoon Kim
|
| 원문수록처(Citation) |
VOL 23 NO. 02 PP. 0001 ~ 0007 (2022. 04) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.
|
| 키워드(Keyword) |
Internet of Things
Quality of Service
Machine Learning
Mobile Edge Computing
Software-Defined Networking
|
| 파일첨부 |
PDF 다운로드
|
