Ahmed Samir Jagmagji, Haider Dhia Zubaydi, Sándor Molnár, and Mahmood Alzubaidi
Utilizing Machine Learning as a Prediction Scheme for Network Performance Metrics of Self-Clocked Congestion Control Algorithm
Congestion Control (CC) is a fundamental mecha- nism to achieve effective and equitable sharing of network fa cilities. As future networks evolve towards more complex para digms, traditional CC methods are required to become more powerful and reliable. On the other hand, Machine Learning (ML) has become increasingly popular for solving challeng ing and sophisticated problems, and scientists have started to turn their interest from rule-based approaches to ML-based methods. This paper employs machine learning models to con struct a performance evaluation scheme to predict network metrics for the Self-Clocked Rate Adaptation for Multimedia (SCReAM) algorithm. It uses a rigorous data preprocessing pipeline and a systematic application of ML methods to en hance the performance of the regression model for SCReAM’s performance metrics. Also, we constructed a dataset that pro vides SCReAM’s input parameters and output metrics, such as network queue delay, smoothed Round Trip Time (sRTT), and network throughput. Each prediction process has several phases: choosing the best initial regressor model, hyperparam eter tuning, ensemble learning, stacking regressors, and uti lizing the holdout data. Each model’s performance was evalu ated through various regression metrics; this study will mainly focus on the coefficient of determination (R2) score. The im provement between the initial best-selected model and the fi nal improved model determined that we were able to increase R2 up to 96.64% for network throughput, 99.4% for network queue delay, and 100% for sRTT.
DOI: 10.36244/ICJ.2024.3.1
Please cite this paper the following way:
Ahmed Samir Jagmagji, Haider Dhia Zubaydi, Sándor Molnár, and Mahmood Alzubaidi, "Utilizing Machine Learning as a Prediction Scheme for Network Performance Metrics of Self-Clocked Congestion Control Algorithm", Infocommunications Journal, Vol. XVI, No 3, September 2024, pp. 2-17., https://doi.org/10.36244/ICJ.2024.3.1