TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Intelligent Massive Traffic Handling Scheme in 5G Bottleneck Backhaul Networks |
| 영문제목(English Title) |
Intelligent Massive Traffic Handling Scheme in 5G Bottleneck Backhaul Networks |
| 저자(Author) |
Prohim Tam
Sa Math
Seokhoon Kim
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| 원문수록처(Citation) |
VOL 15 NO. 03 PP. 0874 ~ 0890 (2021. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput.
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| 키워드(Keyword) |
Cloud Computing
Machine Learning
Massive Traffic
Resource Allocation
Software-Defined Networking
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| 파일첨부 |
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