Towards Fair and QoS‐Aware Bandwidth Allocation in Next‐Generation Multi‐Gigabit WANs

1. Introduction

The exponential growth of network traffic, driven by applications such as video streaming, cloud computing, and IoT, has placed significant demands on wide area networks (WANs). Multi-gigabit WANs, while offering high bandwidth, face challenges in dynamically allocating resources to ensure fairness and meet the diverse QoS requirements of different traffic types. Traditional bandwidth allocation methods often prioritise high-demand or high-priority traffic, leading to the starvation of low-priority traffic and suboptimal network performance.

This paper introduces the Fair and QoS-Aware Dynamic Bandwidth Allocation (FQ-DBA) algorithm, a novel approach designed to address these limitations. FQ-DBA dynamically allocates bandwidth to ensure fairness among users and applications while meeting the QoS requirements of critical traffic. The algorithm leverages traffic classification, fairness enforcement, and QoS-aware allocation to optimise network performance. By integrating fairness and QoS prioritisation, FQ-DBA provides a scalable and energy-efficient solution for next-generation multi-gigabit WANs.

2. Related Work

The increasing demand for high-speed, reliable, and fair network services in multi-gigabit wide area networks (WANs) has driven significant research into dynamic bandwidth allocation (DBA) mechanisms that balance fairness and Quality of Service (QoS). Traditional approaches to bandwidth allocation often prioritise either fairness or QoS, leading to suboptimal performance in scenarios where both are critical. This section reviews key contributions in the areas of fairness mechanisms, QoS-aware bandwidth allocation, and their integration, highlighting the gaps that the proposed Fair and QoS-Aware Dynamic Bandwidth Allocation (FQ-DBA) algorithm aims to address.

Fairness in Bandwidth Allocation: Fairness in network resource allocation has been a long-standing research topic, with early work focusing on max-min fairness and proportional fairness. Max-min fairness, introduced by [1], ensures that the minimum allocation to any user is maximised, while proportional fairness, proposed by [2], balances efficiency and fairness by maximising the sum of logarithmic utilities. These fairness criteria have been widely adopted in various network contexts, including wired and wireless networks. However, they often fail to account for the diverse QoS requirements of modern applications, such as VoIP and video streaming, which require strict latency and jitter guarantees. Recent research has explored adaptive fairness mechanisms that dynamically adjust resource allocation based on network conditions. For example, [3] proposed a weighted fair queuing (WFQ) algorithm that assigns weights to traffic flows based on their priority levels, ensuring that high-priority traffic receives adequate resources while maintaining fairness. Similarly, [4] developed a fairness-aware DBA algorithm for passive optical networks (PONs), which dynamically adjusts bandwidth allocation based on traffic demand and fairness criteria. While these approaches improve fairness, they often lack mechanisms for enforcing QoS guarantees, particularly for latency-sensitive applications.

QoS-Aware Bandwidth Allocation: QoS-aware bandwidth allocation has been extensively studied to meet the stringent requirements of critical applications. Early work by [5] introduced priority queuing and bandwidth reservation mechanisms to prioritise high-priority traffic, such as VoIP and video streaming. These mechanisms ensure that critical traffic receives the necessary resources to meet its QoS requirements, but they often lead to the starvation of low-priority traffic, such as file transfers and web browsing. To address this limitation, [6] proposed a hybrid approach that combines priority queuing with fairness mechanisms, ensuring that low-priority traffic receives a minimum guaranteed bandwidth. Similarly, [7] developed a QoS-aware DBA algorithm for software-defined networks (SDNs), which leverages centralised control to dynamically allocate bandwidth based on traffic demand and QoS requirements. However, these approaches often lack scalability and energy efficiency, which are critical for multi-gigabit WANs.

Integration of Fairness and QoS: The integration of fairness and QoS in bandwidth allocation has been a challenging research problem, as it requires balancing conflicting objectives. Recent advancements in machine learning (ML) and Software-Defined Networking (SDN) have provided new opportunities to address this challenge. For example, [8] proposed an ML-based DBA algorithm that uses reinforcement learning to dynamically allocate bandwidth based on fairness and QoS criteria. Similarly, [9] developed an SDN-based framework that integrates fairness enforcement and QoS-aware allocation, demonstrating significant improvements in network performance. Despite these advancements, existing solutions often lack comprehensive mechanisms for balancing fairness and QoS in large-scale, high-capacity networks. Additionally, they often fail to address energy efficiency, which is increasingly important in modern networks. The proposed FQ-DBA algorithm bridges this gap by integrating fairness enforcement and QoS-aware allocation into a unified framework. By leveraging traffic classification, dynamic bandwidth allocation, and performance optimisation, FQ-DBA ensures equitable resource distribution while meeting the stringent requirements of critical applications.

Energy Efficiency in Bandwidth Allocation: Energy efficiency has become a key concern in modern networks, particularly in large-scale WANs. Research by [10] explored energy-aware bandwidth allocation algorithms that minimise power consumption by consolidating traffic onto fewer network devices. Similarly, [11] proposed a dynamic bandwidth allocation scheme that reduces energy consumption by adjusting link rates based on traffic demand. However, these approaches often trade off energy efficiency for performance and fairness, highlighting the need for integrated solutions that balance these objectives.

3. Proposed Framework For FQ-DBA

3.1. Objectives

The primary objectives of FQ-DBA are:

• Fairness: Ensure equitable bandwidth allocation among users and applications, preventing the starvation of low-priority traffic.
• QoS Awareness: Prioritise traffic based on QoS requirements, such as latency, jitter, and packet loss, for critical applications.
• Efficiency: Optimise bandwidth utilisation to maximise throughput and minimise latency.
• Scalability: Operate efficiently in large-scale, high-capacity multi-gigabit networks.
• Energy Efficiency: Reduce energy consumption in network devices.

3.2. . Key Features

• Traffic Classification: FQ-DBA classifies traffic based on application type (e.g., VoIP, video streaming, file transfer) and QoS requirements, ensuring that high-priority traffic receives the necessary resources.
• Fairness Enforcement: The algorithm calculates the fair share of bandwidth for each user or application and ensures that low-priority traffic receives a minimum guaranteed bandwidth to prevent starvation.
• QoS-Aware Allocation: FQ-DBA implements priority queuing and bandwidth reservation to meet the QoS requirements of critical applications. Bandwidth allocation is dynamically adjusted based on real-time network conditions.
• Dynamic Bandwidth Allocation: The algorithm treats available bandwidth as a shared resource pool and allocates it dynamically based on demand, fairness, and QoS requirements.
• Performance Optimisation: FQ-DBA implements congestion control and latency reduction mechanisms to optimise network performance.

3.3. . Algorithm Components

i. Traffic Classification

• Application Awareness: Classify traffic based on application type and QoS requirements.
• User Fairness: Identify users or traffic flows to ensure fair resource allocation.

ii. Bandwidth Demand Estimation

• Historical Data Analysis: Use historical traffic patterns to estimate bandwidth requirements.
• Real-Time Monitoring: Continuously monitor traffic demands and network conditions to adjust bandwidth allocation dynamically.

iii. Fairness Enforcement

• Fair Share Calculation: Calculate the fair share of bandwidth for each user or application based on predefined fairness criteria (e.g., max-min fairness, proportional fairness).
• Starvation Prevention: Ensure that low-priority traffic receives a minimum guaranteed bandwidth.

iv. QoS-Aware Allocation

• Priority Queuing: Implement priority queues to handle high-priority traffic with strict QoS requirements.
• Bandwidth Reservation: Reserve bandwidth for critical applications to ensure QoS guarantees.
• Dynamic Adjustment: Adjust bandwidth allocation dynamically to meet QoS requirements as network conditions change.

v. Dynamic Bandwidth Allocation

• Resource Pooling: Treat available bandwidth as a shared resource pool and allocate it dynamically based on demand and fairness criteria.
• Load Balancing: Distribute traffic across available paths to prevent congestion and ensure efficient resource utilisation.

vi. Performance Optimisation

• Congestion Control: Implement congestion avoidance mechanisms (e.g., rate limiting, traffic shaping) to prevent network overload.
• Latency Reduction: Optimise path selection and bandwidth allocation to minimise end-to-end latency for latency-sensitive applications.

3.4. Algorithm Workflow

• Initialisation: Classify traffic and initialise monitoring mechanisms.
• Bandwidth Demand Estimation: Estimate bandwidth requirements using historical data and real-time monitoring.
• Fairness Enforcement: Calculate fair shares and allocate bandwidth to ensure fairness.
• QoS-Aware Allocation: Prioritise high-priority traffic and reserve bandwidth for critical applications.
• Dynamic Bandwidth Allocation: Allocate bandwidth dynamically based on demand, fairness, and QoS requirements.
• Performance Optimisation: Monitor network performance and adjust bandwidth allocation to optimise throughput, latency, and resource utilisation.

3.4.1 . Key Workflow Summary

• Initialise: Configure queues, QoS policies, and fairness thresholds.
• Monitor: Capture and classify live traffic.
• Assess Fairness: Compute fairness index across classes.
• Allocate:

• Enforce priority-based bandwidth if fair.
• Redistribute if unfair (fairness < threshold).

5.

Adjust Dynamically:

• Increase bandwidth for congested queues
• Decrease bandwidth for underutilised queues

6.

Repeat: Iterate at fixed intervals.

This algorithm prioritizes fairness and congestion responsiveness while respecting traffic class priorities—ideal for networks requiring balanced QoS (e.g., enterprise/campus networks).

Figure 1. Algorithm Flow.

Figure 1. Algorithm Flow.

3.4.2. FQ DBA Algorithm

Below is the FQ DBA Algorithm:

Algorithm 3: Fair and QoS-Aware Dynamic Bandwidth Allocation (FQ-DBA)
1.	function FQ-DBA(node):
2.	// Initialisation configureQueues()
3.	defineQoSParameters()
4.	setFairnessThreshold()
5.	while simulationRunning:
6.	// Step 1: Real-Time Traffic Capture currentTraffic = captureTraffic(node)
7.	classifiedTraffic = classifyTraffic(currentTraffic)
8.	// Step 2: Calculate Fairness fairnessIndex = calculateFairnessIndex(classifiedTraffic)
9.	// Step 3: Allocate Bandwidth for each trafficClass in classifiedTraffic:
10.	if fairnessIndex<fairnessThreshold: adjustBandwidthAllocations(classifiedTraffic)
11.	if trafficClass == "Real-Time": allocateBandwidth(trafficClass, highPriority)
12.	else if trafficClass == "Interactive": allocateBandwidth(trafficClass, mediumPriority)
13.	else if trafficClass == "Background": allocateBandwidth(trafficClass, lowPriority)
14.	// Step 4: Dynamic Adjustment if queueLengthExceedsThreshold():
15	for each trafficClass in classifiedTraffic:
16	if queueLength(trafficClass) > queueThreshold: increaseBandwidth(trafficClass)
17	else: decreaseBandwidth(trafficClass)
18	wait(timeInterval)

3.4.2. FQ-DBA algorithm Description

Line 1.	▪ Purpose: Main function for fair queuing-based bandwidth allocation. ▪ Parameter: ▪ `node`: Network device (e.g., switch, router) managing traffic queues.
Line 2.	▪ Action: Sets up multiple queues for different traffic classes. ▪ Details: Defines queue properties like buffer size and scheduling policies (e.g., WFQ).
Line 3.	▪ Action: Configures Quality of Service rules: ▪ Priority levels, minimum/maximum bandwidth guarantees per class.
Line 4.	▪ Action: Sets numerical threshold (e.g., 0.0–1.0) to trigger fairness adjustments. ▪ Purpose: Ensures no traffic class starves others (e.g., Jain's fairness index < 0.8).
Line 5.	▪ Loop: Continuously executes until simulation ends.
Line 6.	▪ Action: Monitors live traffic at the node (e.g., packets/bytes per second).
Line 7.	▪ Action: Sorts traffic into classes: ▪ Real-Time: VoIP, video streaming (latency-critical). ▪ Interactive: Gaming, remote desktop (balanced latency/throughput). ▪ Background: Downloads, updates (bandwidth-tolerant).
Line 8.	▪ Action: Computes fairness metric (e.g., Jain's index) across classes. ▪ Output: Value between 0 (unfair) and 1 (perfectly fair).
Line 9.	▪ Loop: Processes each traffic class for allocation.
Line 10.	▪ Condition: Triggers if fairness is below threshold (e.g., <0.75). ▪ Action: Redistributes bandwidth to prevent starvation (e.g., caps greedy classes).
Line 11.	▪ Policy: Assigns highest priority + guaranteed bandwidth (e.g., 50% of total). ▪ Goal: Meets strict latency/jitter requirements.
Line 12.	▪ Policy: Medium priority with moderate guarantees (e.g., 30% of total). ▪ Balance: Throughput without harming Real-Time.
Line 13.	▪ Policy: Lowest priority; uses residual bandwidth (e.g., 20% max). ▪ Behaviour: Throttled during congestion.
Line 14.	▪ Condition: Checks if any queue nears overflow (e.g., >90% full). ▪ Purpose: Prevents packet loss from congestion.
Line 15.	▪ Loop: Re-evaluates each class for queue-based adjustments.
Line 16.	▪ Action: Boosts bandwidth for congested classes temporarily. ▪ Example: +10% to Interactive if its queue is overflowing.
Line 17.	▪ Action: Reduces bandwidth for underutilized classes. ▪ Goal: Frees capacity for needy queues (e.g., shrink Background to aid Real-Time).
Line 18.	▪ Action: Pauses until next cycle (e.g., 1 ms–100 ms). ▪ Trade-off: Shorter intervals = quicker adaptation but higher CPU load.

4. Research Methodology

The methodology establishes a rigorous, reproducible framework for evaluating next-generation DBA algorithms in multi-gigabit WAN environments. By integrating realistic traffic models, validated failure scenarios, and statistically robust analysis techniques, we enable direct comparison of resilience and performance enhancements against industry standards. The ns-3 implementation balances fidelity with practicality, providing actionable insights for real-world deployment while transparently acknowledging scalability constraints.

4.1. Research Philosophy

• Pragmatic Paradigm: Combines quantitative simulation data with qualitative engineering insights to address real-world WAN challenges.
• Design Science Research (DSR): Focuses on designing, developing, and validating four novel DBA algorithms to optimize resilience and QoS.

4.2. Visual Workflow

Figure 3. Visual Workflow.

Figure 3. Visual Workflow.

5. Evaluation Of FQ-DBA

5.1. Evaluation Metrics

The performance of FQ-DBA is evaluated using the following metrics:

• Fairness Index: Measure the fairness of bandwidth allocation among users and applications (e.g., using Jain’s fairness index).
• QoS Compliance: Evaluate the algorithm’s ability to meet QoS requirements for high-priority traffic (e.g., latency, jitter, packet loss).
• Throughput: Total data transmitted successfully over the network.
• Latency: End-to-end delay for data transmission.
• Resource Utilisation: Efficiency of bandwidth and path utilization.
• Recovery Time: Time taken to adapt to changes in network conditions or traffic demands.

5.2. Network Parameters

Parameter	Value
Total Bandwidth	10 Gbps
Number of Flows	100–500 (mixed traffic)
Traffic Types	VoIP, Video, Bulk Data
QoS Requirements	Latency (<50ms), Jitter (<10ms), Min BW Guarantee
Congestion Scenario	Random bursts (50–90% load)

Parameter	Value
Total Bandwidth	10 Gbps
Number of Flows	100–500 (mixed traffic)
Traffic Types	VoIP, Video, Bulk Data
QoS Requirements	Latency (<50ms), Jitter (<10ms), Min BW Guarantee
Congestion Scenario	Random bursts (50–90% load)

6. Simulation Results

6.1. Simulation Setup

To evaluate FQ-DBA, we compare it against baseline algorithms like:

• Static Allocation (Fixed BW per user)
• Proportional Fair (PF) Allocation
• Hierarchical Token Bucket (HTB)

6.2. Fairness Comparison (Jain’s Index)

Table 1. Fairness Comparison.

Algorithm	Jain’s Fairness Index (0–1)
Static	0.65
PF	0.82
HTB	0.78
FQ-DBA	0.94

Table 1. Fairness Comparison.

Algorithm	Jain’s Fairness Index (0–1)
Static	0.65
PF	0.82
HTB	0.78
FQ-DBA	0.94

Figure 4. Fairness Comparison (Jain’s Index).

Figure 4. Fairness Comparison (Jain’s Index).

Finding: FQ-DBA improves fairness by 14–44% over baselines.

6.3. QoS Compliance (% Flows Meeting SLA)

Table 2. QoS Compliance.

Traffic Type	FQ-DBA	HTB	PF
VoIP	98%	85%	76%
Video	95%	80%	72%
Bulk Data	90%	75%	68%

Table 2. QoS Compliance.

Traffic Type	FQ-DBA	HTB	PF
VoIP	98%	85%	76%
Video	95%	80%	72%
Bulk Data	90%	75%	68%

Figure 5. QoS Compliance.

Figure 5. QoS Compliance.

Finding: FQ-DBA ensures 90%+ QoS compliance, outperforming others.

6.4. Throughput Efficiency

Table 3. Throughput Efficiency.

Algorithm	Average. Utilization (%)
Static	70%
PF	85%
HTB	88%
FQ-DBA	93%

Table 3. Throughput Efficiency.

Algorithm	Average. Utilization (%)
Static	70%
PF	85%
HTB	88%
FQ-DBA	93%

Figure 6. Throughput Efficiency.

Figure 6. Throughput Efficiency.

7. Finding: FQ-DBA achieves near-optimal bandwidth utilization.

Latency & Jitter (ms)

Table 4. Latency & Jitter.

Algorithm	Avg. Latency/ms	Max Jitter/ms
Static	45	15
PF	38	12
HTB	32	9
FQ-DBA	28	6

Table 4. Latency & Jitter.

Algorithm	Avg. Latency/ms	Max Jitter/ms
Static	45	15
PF	38	12
HTB	32	9
FQ-DBA	28	6

Figure 7. Latency & Jitter.

Figure 7. Latency & Jitter.

Finding: FQ-DBA reduces latency by 12–38% compared to baselines.