In this lesson, you will learn how to stress-test your networked game by artificially injecting latency, jitter, and packet loss using Unreal Engine’s built-in Network Emulation settings.
Why Simulate Poor Network Conditions?
Even if your character movement logic and RPCs work perfectly on a local loopback connection, they will likely behave unexpectedly when exposed to the reality of the public internet. By forcing the engine to delay or drop packets, you can observe how your interpolation, client-side prediction, and reconciliation systems hold up when they aren't receiving a steady stream of data.
How to Configure Net Emulation
Unreal Engine provides a powerful set of console commands to emulate network degradation. You can access these via the console (tilde key ~) during PIE (Play In Editor) or in a standalone build.
To start testing, use the following commands:
p.NetShowCorrections 1: Enable this first so you can visually see when the client is being forced to snap back to the server's authoritative position.Net PktLag=[Milliseconds]: This adds a fixed delay to every outgoing and incoming packet. Setting this to200effectively simulates a high-ping environment.Net PktLagVariance=[Milliseconds]: This adds "jitter" (fluctuation). If your lag is 100ms and variance is 50ms, your ping will swing between 50ms and 150ms.Net PktLoss=[Percentage]: This drops a specified percentage of packets. Use small values like5(5%) to see how yourReliableversusUnreliableRPCs handle data recovery.
Implementation Workflow
When testing, focus on how your character movement responds to Net PktLoss. If you notice stuttering, check if your NetUpdateFrequency is too low for the erratic delivery of packets, or verify if your client-side prediction reconciliation is triggering too aggressively due to high jitter. Always reset the emulation after your tests by setting these values back to 0, or the engine will continue to drop packets in subsequent play sessions.
Key Takeaway: Use the Net Pkt console commands to validate your game's robustness against high latency and packet loss, ensuring your prediction systems remain stable when the network becomes unreliable.