Machine Learning for Broad-Sensed Internet Congestion Control and Avoidance: A Comprehensive Survey

Huang, Heyu; Zhu, Xiaomin; Bi, Jiedong; Cao, Wenpeng; Zhang, Xinchang

doi:10.1109/access.2021.3060287

Cited by 12 publications

(5 citation statements)

References 127 publications

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“…Moreover, the direct advantages of clustering to improve prediction accuracy through data quality enhancement are explored in [39]. However, these investigations focus on distinct, non-uniformly distributed data.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Cellular Traffic Prediction Using Data Reduction Techniques

Nashaat,

Mohammed,

Abdel-Mageid

et al. 2024

IEEE Access

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Estimating and analyzing traffic patterns become essential in managing Quality of Service (QoS) metrics while assessing internet data traffic in cellular networks. Cellular network planners frequently apply various approaches to predict network traffic. However, most existing studies focus on using the available local data to jointly build prediction models, facing data security challenges and time complexity, especially with multi-dimensional datasets. Therefore, this paper proposes a framework to handle traffic prediction with the considerable potential of Machine Learning (ML) algorithms. An Adaptive Machine Learning-based Cellular Traffic Prediction (AML-CTP) framework is presented to select a suitable ML algorithm for multi-dimensional datasets. Its objective is to streamline and speed up the selection of an appropriate model for predicting network traffic load. The framework employs two density-based clustering algorithms to categorize similar nearby traffic into various clusters, considering data similarity and convergence. Additionally, it assesses data quality and homogeneity by training models with data samples from each cluster to accurately determine the most suitable machine learning model. The optimal model is selected from four supervised predicting algorithms, reducing training time and hardware complexity. Two case studies from a popular telecommunication equipment corporation in Egypt are implemented using reallife cellular traffic with multi-dimensional features. The case studies show that the framework can help reduce the computational cost of training the model and reduce the risk of overfitting. The experimental results show that selecting the best prediction model training could save up to 85% of computational time compared to two state-of-the-art techniques while achieving an accuracy of 98.8%.

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Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Cellular Traffic Prediction Using Data Reduction Techniques

Nashaat,

Mohammed,

Abdel-Mageid

et al. 2024

IEEE Access

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“…Zhang et al ( 2021) provide a survey of decentralized machine learning on blockchain, including the current state-of-the-art, research challenges, and future directions. They also discussed the potential of blockchainbased decentralized machine learning to address the challenges of privacy, security, and data sharing in machine learning [5].…”

Section: Literature Surveymentioning

confidence: 99%

Decentralized Machine Learning on Blockchain: A Review of Recent Developments

2023

IRJMETS

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Decentralized machine learning (DML) is a new paradigm in artificial intelligence (AI) that combines the power of distributed computing and blockchain technology to enable secure and privacy-preserving machine learning. In DML, multiple devices or nodes collaborate to train a machine learning model without sharing their data, thereby enhancing data privacy and security. This research paper provides a comprehensive review of recent developments in DML on the blockchain, including its applications, challenges, and potential solutions. The paper analyzes relevant literature and case studies to highlight the advantages and limitations of DML on the blockchain. The study looks at the many consensus techniques used in DML and how they affect system performance, including proof-of-work, proof-of-stake, and proof-of-authority. The function of smart contracts in DML and how they might improve the system's security and transparency are also discussed in the paper. The paper also covers DML on the blockchain's difficulties and potential solutions, including scalability, interoperability, and privacy issues. According to the study's results, DML on the blockchain has the power to change the AI industry by providing safe and private machine learning. To address the technological and nontechnical problems, however, it also requires additional research and development. To fully realize the potential of DML on the blockchain, the study emphasizes the necessity of a coordinated effort by researchers, developers, and policymakers.

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“…Learning based algorithms have potential to adapt themselves to various networks. Interested readers could refer to References 17‐20, in which the authors review on works applying machine learning approach to solve congestion control problem.…”

Section: Related Workmentioning

confidence: 99%

LearningCC: An online learning approach for congestion control

Zhang

Lei

Zhang

et al. 2021

Trans Emerging Tel Tech

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It is noticed that traditional loss based algorithms are not moving data very well in today's Internet. To get bottleneck bandwidth fully utilized, these algorithms will keep increasing the congestion window and maintain a standing queue of packets in routers, which cause large end to end latency. Such long latency is quite annoying to these ever growing delay sensitive applications. Recently, much effort has been devoted by researchers from both academia and industry to design better congestion control protocols and make a better Internet. In this article, we present an online learning based approach named LearningCC for congestion control. Instead of adjusting the congestion window with fixed rule as these traditional algorithms do, there are several options for an endpoint to choose. Each option is mapped as an arm of a bandit machine. The exploration and exploitation scheme is used to guide the selection on congestion window update rule. Through trial and error, an endpoint improves its performance by choosing action with better reward. Experiments are conducted on ns3 platform to verify the effectiveness of LearningCC. Results indicate it achieves lower transmission delay than loss based algorithms. Especially, we found LearningCC makes significant throughput improvement in link suffering from random loss.

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Machine Learning for Broad-Sensed Internet Congestion Control and Avoidance: A Comprehensive Survey

Cited by 12 publications

References 127 publications

Machine Learning-Based Cellular Traffic Prediction Using Data Reduction Techniques

Machine Learning-Based Cellular Traffic Prediction Using Data Reduction Techniques

Decentralized Machine Learning on Blockchain: A Review of Recent Developments

LearningCC: An online learning approach for congestion control

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