Optimization of Network Performance Using AI-Based Traffic Routing in Federal University, Lafia, Nasarawa State
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- NGN 5000
Background of the Study
Network performance is a critical factor in the success of digital learning and administrative processes within universities. Federal University, Lafia, Nasarawa State, like many higher education institutions, has witnessed an increasing demand for reliable and high-performance network connectivity. The growing reliance on cloud services, online learning platforms, and digital communication tools has put additional strain on university networks, leading to issues such as bandwidth congestion, slow network speeds, and delays in data transfer.
Artificial intelligence (AI) has shown great promise in optimizing network performance through intelligent traffic routing. AI-based solutions can analyze network traffic patterns, identify bottlenecks, and dynamically adjust routing paths to optimize bandwidth usage and reduce latency. Machine learning algorithms, for example, can predict network congestion before it occurs and adjust traffic flow in real-time, ensuring that critical applications receive priority and that resources are efficiently allocated.
The integration of AI into network performance optimization offers several benefits, including reduced network downtime, improved user experience, and efficient utilization of network resources. However, while AI holds promise for improving network performance, its implementation requires careful consideration of the university's existing network infrastructure and the specific needs of students and staff. This study aims to explore the potential of AI-based traffic routing solutions in optimizing network performance at Federal University, Lafia, and assess the impact of such solutions on the university’s overall network efficiency.
Statement of the Problem
Federal University, Lafia, faces significant challenges in managing its network traffic due to increasing demands for bandwidth, especially during peak usage times such as online exams or large virtual lectures. The existing network infrastructure is often unable to accommodate the growing number of simultaneous users and data-intensive activities, leading to congestion, latency, and service interruptions. Traditional network management tools are insufficient to handle the complexities of modern university networks. AI-based traffic routing solutions offer a potential solution to these challenges, but their effectiveness in improving network performance at the university has not been adequately studied. This research seeks to evaluate the impact of AI-driven traffic routing on network performance at Federal University, Lafia.
Objectives of the Study
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To assess the current network performance challenges faced by Federal University, Lafia.
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To design an AI-based traffic routing solution for optimizing network performance at the university.
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To evaluate the effectiveness of AI-based traffic routing in improving network performance, including bandwidth allocation, latency reduction, and overall user experience.
Research Questions
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What are the current network performance challenges at Federal University, Lafia?
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How can AI-based traffic routing be designed to optimize network performance at the university?
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How effective is AI-based traffic routing in improving network performance in terms of bandwidth utilization, latency, and user satisfaction?
Significance of the Study
The study’s significance lies in its potential to improve network performance at Federal University, Lafia, through the application of AI-based solutions. Optimizing network traffic can enhance the overall user experience, ensure reliable access to online resources, and support the growing demand for digital services. The research will provide valuable insights into how AI can be leveraged for network management in educational institutions, potentially serving as a model for other universities facing similar challenges.
Scope and Limitations of the Study
This study will focus on the use of AI for optimizing network performance at Federal University, Lafia, and will specifically address traffic routing and bandwidth optimization. The scope is limited to the university’s local area network (LAN) and does not extend to wider network infrastructure or external network providers. Limitations include the availability of AI tools, network data, and the collaboration of relevant stakeholders at the university.
Definitions of Terms
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AI-Based Traffic Routing: The use of artificial intelligence techniques to dynamically manage and optimize the flow of network traffic to ensure efficient use of bandwidth and resources.
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Latency: The delay or lag in data transmission across a network.
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Bandwidth Utilization: The measure of how effectively the available bandwidth is being used by network traffic.
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