SIP 408 Timeout with JMeter SIP load

  • Genesys Cloud Org: US-East
  • JMeter Version: 5.6.2
  • SIP Sampler: 1.0
  • Load: 500 concurrent calls

Need some help troubleshooting SIP 408 Request Timeout errors occurring at 500 concurrent calls. The SIP server drops connections immediately after INVITE when using JMeter SIP sampler. Config looks correct for standard SIP but fails under load.

I typically get around this by verifying that the SIP trunk configuration in Genesys Cloud is explicitly optimized for high-concurrency scenarios, as default settings often throttle or drop connections under heavy load. While JMeter is powerful, it can generate traffic patterns that differ significantly from real-world SIP user agents, potentially triggering security or capacity limits.

First, ensure your SIP trunk in Genesys Cloud has the maximum concurrent sessions set appropriately. Navigate to Admin > Telephony > SIP Trunks and check the Max Concurrent Calls setting. If this is too low, new INVITEs will be rejected or timed out.

Second, review the JMeter SIP sampler configuration. The default timeout values might be too aggressive. Increase the Response Timeout and Connection Timeout in the SIP Request sampler to at least 5000ms. Additionally, verify that the Keep-Alive mechanism is enabled if your provider supports it, to prevent premature connection closure.

Here is a recommended JMeter SIP Sampler configuration snippet:

Parameter Recommended Value
Response Timeout 5000 ms
Connection Timeout 3000 ms
Reuse Connection True
Keep Alive True

Also, check the Genesys Cloud system logs for the specific SIP trunk during the test. Look for 408 Request Timeout or 503 Service Unavailable errors. If you see 503s, it indicates a capacity issue on the Genesys side or the upstream provider. If you see 408s, it’s likely a network or timeout configuration issue.

Consider running the test in smaller increments (e.g., 100, 200, 300 concurrent calls) to identify the exact threshold where timeouts begin. This helps isolate whether it’s a linear scaling issue or a specific capacity limit being hit.