The Emerging Internet Congestion Control Paradigms

被引：1

作者：

Tafa, Zhilbert ^{[1
]}

Milutinovic, Veljko ^{[2
]}

机构：

[1] Univ Business & Technol, Prishtina, Kosovo

[2] Univ Indiana, Bloomington, IN USA

来源：

2022 11TH MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING (MECO) | 2022年

关键词：

congestion control; computer networks; machine learning; reinforcement learning; TCP; ENHANCEMENT;

D O I：

10.1109/MECO55406.2022.9797207

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper presents a survey on the emerging approaches to the end-to-end congestion control (EECC) in modern Internet. The actual mechanisms are inefficient in the operational contexts of complex and dynamic communication systems. The emerging learning-based approaches employ Machine Learning (ML) to either improve the existing schemes or to completely remodel congestion control from scratch. We classify ML algorithms according to the application area within the context of congestion control, and describe key classes of applications. We also discuss research and engineering issues regarding their implementation and their potential to be included within new operating systems.

引用

页码：718 / 722

页数：5

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