Congestion Management Using K-Means for Mobile Edge Computing 5G System

被引：0

作者：

Ismail, Alshimaa H. ^{[1
]}

Ali, Zainab H. ^{[2
]}

Abdellatef, Essam ^{[3
]}

Sakr, Noha A. ^{[4
]}

Sedhom, Germien G. ^{[5
]}

机构：

[1] Tanta Univ, Fac Comp & Informat, Informat Technol Dept, Tanta 31527, Egypt

[2] Kafrelsheikh Univ, Fac Artificial Intelligence, Embedded Network Syst & Technol Dept, Kafrelsheikh, Egypt

[3] Sinai Univ, Fac Engn, Dept Elect Engn, Al Arish 45511, Egypt

[4] Mansoura Univ, Fac Engn, Comp Engn & Control Syst Dept, Mansoura 35516, Egypt

[5] Delta Higher Inst Engn & Technol, Dept Commun & Elect Engn, Mansoura 35111, Egypt

来源：

WIRELESS PERSONAL COMMUNICATIONS | 2024年 / 136卷 / 04期

关键词：

Congestion control; AGCM; Mobile edge computing; Fog computing; K-means; 5G; ACTIVE QUEUE MANAGEMENT; DESIGN; CONTROLLERS;

D O I：

10.1007/s11277-024-11313-x

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

The congestion management mechanism is essential to manage the explosive evolution of data traffic associated with advanced applications and services in the 5G system. As a result, we suggest a novel methodology to manage congestion for mobile edge computing in the 5G system. Furthermore, the proposed model enhances delay, energy consumption, and throughput. The enhanced random early detection strategy and the K-means approach are used in the suggested model to execute this. Also, a virtual list is realized to maintain packet information and suit more packets. The proposed model is realized in NS2 green cloud simulator. In comparison with the traditional cloud model and the fog computing model, the simulation results confirm that the proposed model reduces delay, boosts throughput, and decreases energy consumption.

引用

页码：2105 / 2124

页数：20

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