Network Traffic Modeling and Optimization in High-Density Environments
DOI:
https://doi.org/10.15662/IJARCST.2020.0303001Keywords:
High-Density Networks, Traffic Modeling, Stochastic Models, Burstiness, Load Balancing, Channel Assignment, OptimizationAbstract
High-density networks—such as urban Wi-Fi deployments, stadiums, airports, and metro environments—present formidable challenges in traffic modeling and optimization due to congestion, interference, and dynamic user behavior. This paper examines historical (pre-2019) approaches to understanding and enhancing network performance under such strained conditions. We review traffic modeling techniques, spanning from stochastic queuing models (e.g., M/M/1, M/G/1), Poisson and non-Poisson arrival models, to analytical and simulation-based methods. Optimization strategies encompass spectrum management, dynamic load balancing, and scheduling—such as adaptive access point selection, clustering, and channel assignment. The proposed research methodology includes defining network scenarios, data collection from high-density testbeds, model calibration, simulation under varied load and mobility patterns, and performance evaluation using throughput, latency, packet loss, fairness, and Quality of Service (QoS) metrics. Key findings from pre-2019 literature demonstrate that Non-Poisson traffic models, especially those capturing burstiness (e.g., heavy-tailed distributions), better mirror real-world behavior than traditional Poisson models. Advanced scheduling and load-balancing algorithms, such as dynamic offloading and distributed channel assignment, significantly improve throughput and reduce collision rates. Workflow includes data gathering, model fitting, simulation, optimization algorithm application, and iterative refinement. Advantages of these techniques include improved resource utilization and user experience; disadvantages revolve around model complexity, computational overhead, and scalability. Results indicate that optimized strategies can yield throughput improvements of 20–50% and latency reduction up to 40% in crowded settings. In conclusion, while early modeling and optimization laid a strong foundation for managing dense networks, evolving user patterns necessitate further enhancements. Future directions propose integrating machine learning for predictive traffic modeling, software-defined networking (SDN) for dynamic control, and edge computing to assist real-time optimization. This work provides both a historical perspective and practical roadmap for future development in high-density network optimization.
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