# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 1 Introduction  
In recent years, the demand for high-speed data transmission, efficient resource management, and network communication has undergone a significant rise due to the rapid growth in the numbers of connected devices and data-intensive applications, leading to efforts to improve the performance of data transmission queueing systems and giving rise to congestion, latency, and dynamic bandwidth allocation challenges [1]. Due to the conflict between the growth in traffic and the constraints on bandwidth, it is crucial to utilize bandwidth resources efficiently to enhance the Quality of Service (QoS) [2]. Modern networks rely on advanced scheduling algorithms and optimized queueing mechanisms to address these issues, support performance, and ensure fair resource distribution. As networks evolve, achieving scalability, flexibility, and reliability remains an important goal for researchers and industry professionals [3]. Unfair resource allocation can lead to resource waste, starvation, or redundancy [4]. A system that allows devices to communicate and exchange data efficiently is known as a network. Networks vary in size and design, ranging from Local Area Networks (LANs) to Wide Area Networks (WANs). Routers serve as key components of network communication as they direct data packets between networks. Through network communication, routers manage traffic using queueing systems, which control how packets are processed and transmitted [5]. A queuing system is essential for handling varying traffic loads, reducing congestion, and ensuring smooth data flows. Different models, such as M/M/1, M/M/c, and G/G/1, determine how packets arrive, wait, and are then served in network queues. These models can optimize performance and reduce delays, thus supporting the design of efficient traffic management strategies [6]. Scheduling algorithms are implemented in routers to efficiently manage packets in queues [3]. These algorithms determine the packet processing order and aim to strike a balance between fairness and efficiency. Basic scheduling methods, such as First-In-First-Out (FIFO), process packets consecutively, whereas Priority Queuing (PQ) prioritizes essential traffic, such as real-time voice or video data. More advanced approaches, such as Weighted Fair Queuing (WFQ) [2,3] and Deficit Round Robin (DRR), ensure proportionate bandwidth allocation among traffic classes. These are crucial in achieving the desired QoS, optimizing bandwidth usage, and improving network reliability [3]. Reinforcement Learning (RL) is commonly used in resource allocation to optimize decision-making in dynamic and unsteady environments. Reinforcement learning-based resource allocation enables the efficient distribution of bandwidth, computing resources, scheduling priorities, and networking [1,4,7]. According to the aim of this paper, while effective in ensuring fair bandwidth distribution, traditional Weighted Fair Queuing (WFQ) systems struggle to adapt dynamically to varying network traffic patterns, such as bursty or periodic flows. Due to its adaptive learning capabilities, deep Reinforcement Learning (DRL) has become a profitable solution for dynamic bandwidth allocation. Regardless, typical DRL models suffer from catastrophic forgetting when exposed to new traffic scenarios, requiring frequent retraining. This limits scalability and robustness in real-time networking environments. Thus, there is a critical requirement to enhance DRL-based WFQ systems with continual learning mechanisms that can retain earlier obtained scheduling policies while adapting to new network conditions efficiently and without forgetting, therefore enabling a more intelligent and scalable bandwidth allocation strategy. The remainder of this paper is organized as follows: Section 2 includes related work, Section 3 produces a contribution of this research, Section 4 describes a methodology for the proposed system model. Section 5 provides the results and analysis. Section 6 discusses the model parameters and the results. Section 7 includes the conclusions.