Development of an Adaptive Network Load Balancing Mechanism for Federal University, Lokoja, Kogi State
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- NGN 5000
Background of the Study
Network load balancing is crucial for optimizing performance and ensuring the efficient distribution of traffic across multiple network resources. Traditional load balancing mechanisms use static algorithms, such as round-robin or least-connections, which may not dynamically adjust to varying network conditions. Adaptive load balancing mechanisms leverage machine learning and real-time monitoring to optimize traffic distribution, reduce congestion, and enhance overall network efficiency.
At Federal University, Lokoja, increasing internet usage, online learning platforms, and cloud-based services have led to frequent network congestion. A static load balancing approach often results in inefficient bandwidth utilization, slow response times, and poor user experience. The implementation of an adaptive network load balancing mechanism can significantly improve network performance by dynamically allocating resources based on real-time demand and traffic conditions.
Statement of the Problem
The current network infrastructure at Federal University, Lokoja, faces performance bottlenecks due to inefficient load balancing techniques. Traditional static load balancers fail to account for real-time network changes, leading to congestion, downtime, and uneven resource distribution. These challenges impact academic and administrative activities that rely on stable internet access.
Existing solutions do not provide real-time adjustments to fluctuating network demands, making them unsuitable for a dynamic university environment. There is a need for an intelligent, adaptive load balancing mechanism that can optimize network traffic distribution, enhance scalability, and improve user experience. This study aims to develop a machine learning-driven adaptive load balancing system to address these challenges.
Objectives of the Study
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To develop an adaptive network load balancing mechanism for Federal University, Lokoja.
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To evaluate the effectiveness of the adaptive mechanism in optimizing traffic distribution and reducing congestion.
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To analyze the scalability and efficiency of the proposed system in a university network environment.
Research Questions
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How does an adaptive load balancing mechanism improve network performance compared to static load balancing?
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What machine learning techniques can be applied to optimize network traffic distribution?
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How scalable is the proposed adaptive load balancing mechanism for a university network?
Scope and Limitations of the Study
This study focuses on developing an adaptive load balancing mechanism for Federal University, Lokoja, Kogi State. It will assess traffic distribution, congestion reduction, and response times. The study will not include comparisons with commercial solutions or off-campus networks. Limitations include real-time testing constraints and variations in network traffic patterns.
Definitions of Terms
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Load Balancing: The process of distributing network traffic across multiple servers to prevent congestion.
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Adaptive Mechanism: A dynamic system that adjusts parameters based on real-time network conditions.
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Machine Learning: A subset of AI that enables systems to learn from data and make decisions.
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