Machine Learning-based fixed access Network Interface Congestion Prediction (Pakistan Journal of Engineering, Technology and Science, 2024)
Network interface congestion refers to a situation in which network interfaces or communication channels within a network experience high levels of traffic, leading to performance degradation or potential network failures. In modern networking, this issue remains relevant and can manifest in various contexts due to a surge in data traffic, Internet of Things (IoT) devices can generate substantial data, increased use of VPNs for remote work and when multiple users or applications concurrently access and transfer data in and out of the cloud. Efforts to forecast and address network interface bottlenecks are crucial to sustaining the performance and consistency of modern networks. In the PTCL fixed access network infrastructure, network blocking is a serious problem that necessities to be addressed. The main objective of this study is to overcome network congestion problems that occur in PTCL's fixed access network through the use of machine learning techniques. Despite having real-time monitoring tools such as SolarWinds, there remains a dearth of software capable of predicting imminent bottlenecks in the network interface. The primary aim is to build an analytical model which can predict potential blockages within 30 days and facilitate proactive management strategies for better administration. Top of FormThe study classifies the most effective machine learning technique for this purpose and evaluates its performance against current methods. To achieve the goal, a diagnostic dataset was created, leveraging appropriate configurations, software tools, and hardware resources. Data was sourced from SolarWinds, consisting of information from over 23,000 interfaces across 11,000 access network nodes. This data was analyzed in PTCL's Data Warehouse, where a historical aggregated dataset was constructed. Exploratory data analysis was performed using Python libraries, including Matplotlib, NumPy, Pandas, and Seaborn. This investigation sets the foundation for proactive network management and enhanced user experiences within PTCL's fixed access network, addressing the critical essential for advanced predictive tools to combat congestion and enhance network competence.
Keywords: Machine Learning, Bandwidth Utilization, Fixed Access Network, Congestion Prediction