QoS-Aware Routing and Resource Allocation Techniques for Enhanced Network Performance
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Abstract
The importance of Quality of Service (QoS) remains of utmost importance in the endeavor to provide high-quality network services. This study focuses on the important area of Quality of Service (QoS) in network services. Specifically, it explores QoS-Aware Routing and Resource Allocation techniques, with a particular emphasis on Class-Based Weighted Fair Queuing (CBWFQ). Our research utilizes the NS-3 simulator to thoroughly assess network performance by analyzing crucial parameters such as latency, throughput, and reliability. We draw insights from the CAIDA Anonymized Internet Traces dataset. CBWFQ, an advanced queuing mechanism, is highlighted for its capability to intelligently categorize and prioritize network traffic into separate classes, each with customized weightings and resource guarantees. The outcomes derived from our experimentation demonstrate significant enhancements in latency, throughput, and reliability across various scenarios, confirming the efficacy of CBWFQ in optimizing resource allocation and guaranteeing superior QoS. This research not only tackles the immediate difficulties encountered by network administrators, but also provides valuable insights for service providers and researchers aiming to enhance network performance in the face of diverse traffic patterns. In addition, we propose potential areas for future investigation, including the examination of AI-driven QoS mechanisms and adaptable strategies that can effectively navigate the constantly changing network environments. The incorporation of QoS methodologies with cutting-edge technologies, such as 5G and future iterations, presents a promising opportunity to improve network management and performance in the upcoming era.
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