to Network Congestion and Buffer Overflow
Simulating DropTail vs. RED Queue Disciplines in NS3
1. Introduction
Once the buffer reaches its maximum capacity, any additional incoming packets are dropped, resulting in buffer overflow. This leads to packet loss, increased queuing delay, reduced throughput, and overall degradation in network performance. Efficient queue management techniques are therefore essential to control congestion and maintain network stability.
2. Queue Management Techniques: DropTail vs. RED
Queue disciplines define how packets are stored and discarded in a router buffer. The design and operational goals behind passive and active queue management are fundamentally divergent:
DropTail Queue (Passive Queue Management)
DropTail follows a simple First-In-First-Out (FIFO) approach. Packets are accepted until the buffer is completely full. Once full, all incoming packets are dropped until space becomes available. This results in sudden packet loss bursts and poor congestion signaling to the end systems.
Random Early Detection (RED) (Active Queue Management)
RED is an advanced queue management technique that monitors the average queue size and begins dropping packets probabilistically before the buffer becomes full. This early detection mechanism helps signal congestion to the sender (forcing TCP windows to shrink or flagging packet distribution changes), preventing abrupt overflow and improving overall network performance.
3. Performance Metrics
To evaluate and compare the effectiveness of DropTail and RED, the following key performance indicators are utilized during validation:
- Throughput: The rate at which data is successfully received at the destination over time. Higher throughput indicates better utilisation of network resources.
- Packet Loss Ratio (PLR): The percentage of packets dropped due to congestion and buffer overflow. Lower PLR indicates better congestion handling.
- Queue Length Behaviour: The variation in buffer occupancy over time. Stable queue behaviour indicates efficient congestion control.
- End-to-End Delay: The total time taken for a packet to travel from source to destination. Increased congestion leads to higher delays.
4. Simulation Scenario and Network Topology
The experiment is conducted using a multi-node network topology designed to intentionally create congestion at a bottleneck link. The topology consists of multiple sender nodes transmitting high-rate UDP traffic towards a single receiver node through two intermediate routers. The access links connecting the senders to the first router operate at high bandwidth, while the link between the two routers is configured as a low-bandwidth bottleneck.
Due to the mismatch between the incoming traffic rate and the bottleneck capacity, packets accumulate at the router queue, leading to buffer overflow. The exact same topology is simulated twice—once using DropTail and once using RED—to observe differences in baseline behaviour.
5. Simulation Parameters
The experiment is modelled using the NS-3 discrete-event network simulator, with network traffic data captured and analysed via the FlowMonitor module. Environmental constraints and protocol configurations include:
| Parameter | Assigned Value |
|---|---|
| Simulator Environment | NS-3 |
| Traffic Type | UDP |
| Number of Senders | 2 Senders |
| Access Link Bandwidth | 10 Mbps |
| Access Link Delay | 2 ms |
| Bottleneck Link Bandwidth | 1 Mbps |
| Bottleneck Link Delay | 10 ms |
| Queue Discipline | DropTail / RED (Toggled via flag) |
| Simulation Time | 15 seconds (Data tracked for 10s window) |
| Traffic Rate | 5 Mbps per sender (10 Mbps cumulative load) |
6. Implementation Source Code
The simulation script is written in C++ using the structural modules of NS-3. Copy or reference this source code inside your local scratch/ directory:
./ns3 run scratch/fifo.ccTo run the active RED alternative, pass the dynamic parameter:
./ns3 run "scratch/fifo.cc --useRed=true"7. Outputs and Multi-Tool Analysis
Animation (NetAnim / Python Visualization)
Using NetAnim, the spatial configuration shows packet streams converging smoothly from the high-speed links onto Router 0, while the queue status indicators visually emphasize backlog buildup preceding the bottleneck link to Router 1.
Graph (Throughput vs. Time)
Plotting the output files generated during the execution runtime maps a stark operational difference. DropTail charts clear performance cliffs, whereas RED presents a flatter, uniform data line.
Wireshark Analysis
Wireshark was utilised to inspect packet-level transmission intervals using standard PCAP files generated natively by NS-3. Deep-dive captures show precise sequences of continuous drop periods in DropTail versus randomised drops in RED.
8. Key Results and Inference
In the DropTail scenario, the queue fills completely before any packets are dropped. This results in sudden bursts of packet loss, increased delay, and unstable throughput. Since packet drops only happen after the buffer is entirely full, there is no early indication of congestion, leading to inefficient network utilisation and traffic synchronisation issues.
In contrast, RED begins dropping packets before the buffer reaches full capacity. This probabilistic early dropping helps regulate the incoming traffic rate and prevents abrupt congestion bottlenecks. As a result, the queue length remains more stable, packet loss is distributed evenly, and throughput remains relatively consistent throughout the execution duration.
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| NetAnim for Queue |
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