NGHIÊN CỨU, CẢI TIẾN CHIẾN LƯỢC HÀNG ĐỢI RED DỰA TRÊN VIỆC TINH CHỈNH NGƯỠNG DƯỚI
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Ngày nhận bài: 19/10/21                Ngày hoàn thiện: 19/04/22                Ngày đăng: 21/04/22Tóm tắt
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PDFTài liệu tham khảo
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[9] A. H. Ismail, A. EL-Sayed, I. Z. Morsi, and Z. Elsaghir, “Enhanced Random Early Detection (ENRED),” International Journal of Computer Applications (0975 – 8887), vol. 92, no. 9, pp. 20-24, April 2014.
[10] A. M. Alakharasani, M. Othman, A. Abdullah, and K. Y. Lun, An Improved Quality-of-Service Performance Using RED’s Active Queue Management Flow Control in Classifying Networks, 2017.
[11] M. M. Abualhaj et al., “FLRED: an efficient fuzzy logic based network congestion control method,” Neural Computing and Applications, vol. 30, no. 3, pp. 925-935, November 2016.
[12] M. Khatari and G. Samara, "Congestion Control Approach based on Effective Random Early Detection and Fuzzy Logic," MAGNT Research Report, 2015.
[13] J. Song and Z. Zhixue, "Research on the Improvement of RED Algorithm in Network Congestion Control," Applied Mechanics and Materials, vol. 713, pp. 2471-2477, 2015.
[14] Z. Yuhong et al., “An Improved Algorithm of Nonlinear RED Based on Membership Cloud Theory,” Chinese Journal of Electronics, vol. 26, no. 3, pp. 538-543, May 2017.
[15] M. Abdulkareem et al., “EFRED: Enhancement of Fair Random Early Detection Algorithm,” International Journal of Communications Network and System Sciences, vol. 8, pp. 282-294, July 2015.DOI: https://doi.org/10.34238/tnu-jst.5180
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