# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 2 Related Work  
Various scheduling disciplines are available for managing separate queues while maintaining fairness and balance in terms of Quality of Service (QoS). In communication networks, WFQ is a crucial scheduling discipline used to ensure fairness and QoS. Using a weight-based bandwidth allocation approach, WFQ assigns weights to various queues based on packet length, thereby ensuring equitable distribution of bandwidth [8]. A technique called PQ, which prioritizes the queue order, often causes resource starvation for lower-priority queues by serving the highest-priority option among non-empty queues. Simulation results show that PQ has a smaller memory queue size than WFQ and FIFO, and a more significant delay [9]. Scheduling disciplines such as WFQ cannot dynamically allocate bandwidth and must learn about the status of queues. The WFQ method has been extensively studied in the literature, with a focus on performance evaluation. Severalmetrics are typically used with the WFQ system, including the mean queue length, system throughput, and mean response time. It would be interesting to compare the performance of a WFQ system with a finite buffer to that of a WFQ system with an infinite buffer [10]. The authors of [3] presented mechanisms and approaches to implementing QoS services in packet networks and created and examined a network model that facilitated data transfer associated with various traffic classes. Traffic shaping techniques based on the WFQ method were utilized for QoS. In individual work, the study in [11] thoroughly investigated traffic scheduling in IPv4 and IPv6 networks for multimedia applications, examining the effectiveness of various scheduling methods for different types of traffic, including HTTP and FTP. Among the different approaches considered (WFQ, FIFO, PQ), WFQ was found to be the best option in terms of its improved performance over the other algorithms, as it offered suitable outcomes over both IPv4 and IPv6. In [12], a packet-scheduling technique was developed that combined WFQ with PQ to decrease latency, significantly improving network performance compared to scenarios where packet scheduling was not employed. By accounting for the extra waiting periods caused by variations in arrival rates, the proposed model was able to lower the estimation error. Even when traffic utilizes the exact network channel or session, it is necessary to consider a complex mix of network and application traffic. The authors also proposed an approximate analytical technique for this purpose. In these research works, specified weights were allocated based on traffic class or priority due to the absence of adaptability to real-time network requirements. In [13], the QoS performance of the WFQ algorithm was investigated and evaluated, and various QoS results were examined. The average throughput, packet arrival ratio, packet loss ratio, and average latency were calculated. The study in [14] considered QoS factors such as jitter, delay, and packet loss probability and evaluated these for algorithms using PQ, FIFO, custom queuing, fair queueing, and WFQ. Given that PQ is usually regarded as the least efficient choice, it is recommended to employ it sparingly in situations where there is little chance of channel erasure [15]. The simulation referenced the approximation terms presented by [PERSON] and [PERSON] for the mean response times in a two-class (ideal) WFQ system with a Poisson arrival process and exponentially distributed service times. The approximation is usually based on an arithmetical approach, and some decisions on how to approximate the behavior occasionally appear to be made ad hoc [16]. These approaches described above rely on predefined controls for resource allocation, which may limit their ability to adapt dynamically to unexpected changes in traffic patterns. Furthermore, these approaches may face challenges in regard to optimizing real-time performance compared to techniques that leverage predictive analysis and adaptive learning.