How to Understanding Doom’s Networking Model and the Rise of L4S

Doom and the Networking Lessons of the 1990s

The release of Doom in 1993 revolutionized multiplayer gaming and introduced real-time networking challenges. By relying on IPX broadcasts, Doom worked well in small LANs but caused broadcast storms in larger networks, crippling performance. This highlighted scalability issues and showed how protocol choices could impact network stability and application performance.

The Birth of Doom and Multiplayer Gaming

On December 10, 1993, the world of gaming changed forever with the release of Doom. It was one of the first widely popular first-person shooters, introducing immersive 3D graphics and intense action that quickly captivated players. But perhaps more importantly from a networking perspective, Doom brought something revolutionary: multiplayer gameplay over a local area network (LAN).

Instead of battling only computer-generated enemies, players could now compete or collaborate with friends connected to the same Ethernet LAN. This innovation made Doom not just a milestone in gaming history but also an early experiment in real-time networked applications.

Why Doom Used IPX Instead of TCP/IP

At the time, the internet and TCP/IP were not as universally adopted as they are today. Instead, Novell’s IPX (Internetwork Packet Exchange) protocol was widely used in corporate and university networks. The developers of Doom, lacking deep networking expertise, chose IPX broadcasts as the mechanism to share player position and action data.

This meant that each player’s machine would broadcast updates about movement, shooting, and other in-game actions to every computer on the LAN. The assumption was simple: if you were on the LAN, you were probably playing Doom.

The Downside: Broadcast Storms in Large Networks

While this approach worked well for small LANs in homes or dorm rooms, it became problematic in larger environments. Some enterprises and universities ran large IPX-based networks with thousands of connected computers.

When Doom packets were broadcast across these networks, the result was chaos:

• Every computer, whether playing Doom or not, received the broadcast packets.
• The sheer volume of traffic led to network slowdowns and even meltdowns.
• IT administrators had to step in, often banning or restricting Doom traffic on their networks.

This situation was an early demonstration of how poorly designed network protocols can scale poorly when moved from small controlled environments to large, real-world infrastructures.

Networking Lessons from Doom

From a modern computer networking perspective, Doom teaches us several important lessons:

1. Broadcast-based communication does not scale: What works on a small LAN can wreak havoc in a large network.
2. Protocol choice matters: Doom’s reliance on IPX instead of TCP/IP limited interoperability and created unintended consequences.
3. Applications must respect shared resources: Networks are common utilities, and bandwidth must be used efficiently to prevent disruption.
4. Real-time applications drive innovation: Doom showed that users demanded low-latency multiplayer experiences, pushing networks toward supporting new workloads.

In many ways, the challenges Doom introduced in the 1990s mirror the challenges modern internet applications face today—especially when it comes to latency management. This leads us to the second part of our discussion: L4S (Low Latency, Low Loss, Scalable throughput).

Part 2: From Bandwidth to Latency—the New Metric

Initially, internet performance was measured mainly by bandwidth, with speed dominating user expectations. As technology advanced and 100 Mbps connections became common, bandwidth ceased to be the bottleneck. Instead, latency emerged as the critical metric, directly affecting responsiveness in real-time applications such as gaming, video conferencing, cloud computing, and virtual reality.

For decades, the primary way to measure internet quality was bandwidth—how many megabits per second (Mbps) could be transmitted. In the 2000s, the goal was often to provide “faster” internet with higher throughput.

However, as technology advanced, broadband, fiber, and wireless solutions began to routinely provide 100 Mbps or more.

At these levels, simply adding more bandwidth no longer guarantees a better experience for applications like:

• Cloud gaming (Google Stadia, Xbox Cloud Gaming, GeForce NOW)
• Virtual reality and augmented reality (VR/AR)
• Real-time video conferencing
• High-frequency trading
• Interactive remote learning

In all these cases, the main bottleneck is not bandwidth—it is latency.

What is Latency?

Latency is the time it takes for a packet to travel from source to destination. High bandwidth but high latency means that while large amounts of data can be transferred, the responsiveness of real-time applications suffers.

For example:

• A 100 Mbps connection with 100 ms latency may feel sluggish for gaming.
• A 20 Mbps connection with 5 ms latency may feel much smoother.

Thus, optimizing latency has become the next big challenge for the networking community.

Part 3: Introducing L4S – Low Latency, Low Loss, Scalable Throughput

The IETF developed L4S to address modern latency challenges. Unlike traditional methods that rely on packet loss, L4S uses Explicit Congestion Notification (ECN) to manage congestion proactively. This approach minimizes queuing delays, reduces loss, and ensures scalable throughput, making it ideal for cloud gaming, VR, streaming, and latency-sensitive internet applications.

What is L4S?

The IETF (Internet Engineering Task Force) has developed L4S: Low Latency, Low Loss, Scalable throughput, an architecture designed to reduce latency while maintaining throughput and avoiding packet loss.

Instead of focusing only on increasing bandwidth, L4S introduces new congestion control mechanisms that ensure:

1. Low Latency – minimizing delay in packet transmission.
2. Low Loss – reducing packet drops caused by congestion.
3. Scalable Throughput – ensuring that applications can use available bandwidth efficiently.

How L4S Works

Traditional congestion control methods rely on packet loss as a signal of congestion (e.g., TCP Reno, TCP Cubic). But packet loss introduces delay and instability.

L4S replaces this with ECN (Explicit Congestion Notification) at the IP layer, which allows routers to mark packets instead of dropping them. End hosts then react quickly to these signals, adjusting transmission rates before congestion builds up.

This results in:

• Microsecond-scale queuing delays
• Stable throughput even under high load
• Smooth interaction between multiple L4S-enabled flows

Real-World Applications of L4S

Several organizations have begun experimenting with L4S in real-world environments.

Its potential benefits are significant:

• Online Gaming: Multiplayer games can achieve near real-time responsiveness, eliminating lag spikes.
• Virtual Reality: L4S could enable seamless VR experiences by keeping motion-to-photon latency extremely low.
• Video Streaming: Smooth playback with minimal buffering, even on congested networks.
• Cloud Computing: Faster interactions with cloud-based applications.
• Telemedicine: Real-time diagnosis and consultations without delay.

In essence, just as Doom highlighted the importance of latency for enjoyable gaming in 1993, L4S is pushing the boundaries of how we design the internet for latency-sensitive applications today.

Part 4: Doom vs. L4S – A Networking Perspective

Doom represents the past of networked applications, relying on broadcast-based communication that failed to scale. L4S represents the future, emphasizing low latency and efficient congestion control. Both highlight different eras of networking challenges—Doom’s broadcast storms and L4S’s latency solutions—showing how real-world applications drive improvements in networking architectures over time.

It is fascinating to compare Doom and L4S as two points in the timeline of network evolution.

Doom’s networking issues were born out of a lack of scalability. L4S, on the other hand, is being developed precisely to ensure scalability of low-latency networking for the future.

Part 5: What Students Can Learn

Students studying computer networks can gain valuable insights from both Doom and L4S. Doom demonstrates the risks of protocol misuse and scalability issues, while L4S illustrates modern innovations for latency management. Together, they provide excellent case studies that link history, theory, and practice—helping students better understand real-world networking challenges and solutions.

For students studying computer networks, both Doom and L4S provide excellent case studies:

1. Understand historical context – Learning how Doom used IPX helps explain why certain protocols thrived while others faded.
2. Appreciate design trade-offs – Doom developers optimized for simplicity, but at the cost of scalability. L4S architects are optimizing for latency without sacrificing throughput.
3. See the link between applications and networks – Applications like Doom or VR don’t just use networks—they drive their evolution.
4. Prepare for the future – Concepts like ECN, congestion control, and latency optimization are key skills for next-generation network engineers.

At computernetworkassignmenthelp.com, we emphasize that assignments are not just about solving textbook problems but about connecting networking theory to real-world systems. Doom and L4S are perfect examples of how theoretical ideas translate into practical impact.

Conclusion

From the chaos caused by Doom’s IPX broadcasts in the 1990s to the precision engineering of L4S for tomorrow’s internet, the history of networking has always been about balancing performance, scalability, and user experience.

As bandwidth continues to grow, latency has emerged as the defining metric for modern applications. Just as Doom showed us the dangers of ignoring scalability, L4S shows us the possibilities when we design networks with latency optimization in mind.

For students, researchers, and professionals alike, these stories underline an important truth: networking is not static—it evolves with the demands of its users. By studying both the past and the future, we can better prepare to design, optimize, and troubleshoot the networks of tomorrow.

At computernetworkassignmenthelp.com, our mission is to guide students through this fascinating journey. Whether you are working on protocol analysis, congestion control, or case studies of real-world applications, our team is here to provide clarity, academic support, and deep insights into how networks truly work.