Performance analysis techniques for congestion issues in computer networks
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Key Performance Metrics for Congestion Analysis in Computer Networks
Performance analysis of congestion in computer networks relies on monitoring several key metrics. These include throughput, packet loss, jitter, delay, bandwidth utilization, and buffer size. These metrics help identify when congestion occurs and how it impacts network performance, such as increased latency and reduced data delivery efficiency Purnawansyah2024Akhtar2024Liu2017+1 MORE.
Simulation and Analytical Tools for Congestion Performance Evaluation
Simulation tools like IxChariot and MATLAB are widely used to emulate network conditions and analyze congestion scenarios. These tools allow researchers to test different network topologies, traffic loads, and congestion control algorithms by measuring performance metrics under controlled conditions Purnawansyah2024Liu2017. Analytical models, such as nonlinear system models and control theory approaches, are also used to evaluate and compare congestion control protocols Liu2017Srivastava2024.
Machine Learning and Deep Learning Techniques for Congestion Prediction
Recent advancements leverage machine learning and deep learning for proactive congestion prediction. Techniques such as neural networks, ensemble learning (combining models like LSTM and CNN), and reinforcement learning have shown high accuracy in predicting congestion and enabling proactive management. These models analyze historical and real-time network data to forecast potential bottlenecks, allowing network administrators to take preventive actions before congestion impacts performance Kushwaha2024Chiu1989Sahu2017+1 MORE.
Congestion Control Algorithms: Comparative Analysis
Various congestion control algorithms have been developed and compared using performance metrics like efficiency, fairness, convergence time, and stability. Additive Increase Multiplicative Decrease (AIMD) algorithms are widely recognized for their ability to achieve efficient and fair network states. Other protocols, such as Westwood+, New Reno, BIC TCP, CUBIC, FAST, and XCP, have been evaluated for their performance in both conventional and high-speed networks, with each offering different trade-offs in terms of throughput, delay, and responsiveness Liu2017Ismaeel2023.
Quality of Service (QoS) and Adaptive Techniques
Quality of Service (QoS) techniques, such as bandwidth allocation, packet prioritization, and the use of playout buffers, are effective in managing congestion and improving user experience. Adaptive control methods, including prescribed performance control and backstepping H∞ tracking, can guarantee both transient and steady-state performance, ensuring that network queues remain within desired limits even under varying traffic conditions Purnawansyah2024Srivastava2024.
Advanced Detection and Routing Strategies
Innovative detection methods, such as multiwavelet-based approaches, can quickly and efficiently identify shared congestion points in the network, reducing the need for excessive probe packets and minimizing network overhead . Congestion-aware routing algorithms, like reinforcement-based Q-routing, dynamically adjust routing decisions based on real-time congestion information, leading to significant reductions in packet latency and energy consumption compared to traditional routing methods .
Conclusion
Performance analysis techniques for congestion issues in computer networks have evolved from traditional metric-based monitoring and simulation to advanced machine learning, deep learning, and adaptive control strategies. By combining accurate performance metrics, predictive modeling, and adaptive congestion control algorithms, network administrators can better detect, predict, and manage congestion, ensuring efficient and reliable network operations even under high traffic demands Purnawansyah2024Kushwaha2024Akhtar2024+7 MORE.
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Most relevant research papers on this topic
Congestion Predictive Modelling on Network Dataset Using Ensemble Deep Learning
Ensemble deep learning methods, particularly LSTM-CNN, consistently outperform individual deep learning models in predicting network congestion, enhancing network efficiency and reliability.
Machine Learning-based fixed access Network Interface Congestion Prediction
Machine learning techniques can predict potential network interface congestion in PTCL's fixed access network within 30 days, enabling proactive management strategies and improved user experiences.
Performance analysis of congestion-aware Q-routing algorithm for network on chip
The congestion-aware Q-routing (CAQR) algorithm significantly improves performance in network on chip systems compared to conventional XY routing methods, reducing average packet latency and energy consumption.
Multiwavelet based-approach to detect shared congestion in computer networks
The new multiwavelet-based detection technique improves shared congestion detection in computer networks by providing faster convergence and reducing probe packet overload, ultimately improving overall performance.
A Technique for Prediction of Network Congestion Using Neural Network
This paper proposes a neural network-based technique for predicting network congestion, enabling network administrators to avoid issues before they impact performance, with an average classification rate of 99%.
Prediction of Network Congestion at Router using Machine learning Technique
Machine learning techniques can effectively predict and prevent network congestion at routers, improving performance and reducing packet loss.
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