- Environment: Genesys Cloud (v2023-10), 15 BYOC Trunks, Singapore Region
- SDK: Genesys Cloud Java SDK 11.5.2
- Architecture: Primary/Secondary Carrier Failover
- Tooling: Genesys Cloud Reporting API v2
Can anyone clarify why the sip_trunk_failover_duration metric in the Reporting API shows a 4-second average, while the raw SIP logs indicate immediate 408 timeouts and sub-second rerouting?
We manage a complex failover logic across multiple APAC carriers. The SIP traces clearly show the primary trunk returning a 408 Request Timeout within 200ms, and the secondary trunk accepting the INVITE within 300ms. However, the aggregated analytics dashboard reports a significant latency spike, averaging 4-5 seconds per failed transaction during our peak load tests.
This discrepancy is causing false positives in our carrier SLA monitoring scripts. We have verified the time synchronization on all trunk endpoints, and the NTP skew is less than 1ms. Is there a known aggregation lag or specific filtering logic in the interaction endpoint that might be capturing the initial retry attempts as part of the failover duration? We need to ensure our reporting accurately reflects the actual network performance rather than internal processing delays.
Make sure you align your data collection windows with the actual SIP transaction lifecycle, as the Reporting API aggregates metrics on a fixed 5-second bucket by default. The discrepancy you are seeing usually stems from how the platform calculates failover duration versus how your carrier logs record 408 timeouts. When a 408 is received, the WebRTC stack or SIP proxy initiates the reroute immediately, which happens in milliseconds. However, the reporting engine waits for the session state change to propagate through the analytics pipeline before committing the metric. This creates a lag where the “duration” includes the processing overhead of the reporting service, not just the network latency.
To get a more accurate picture, try querying the real-time event stream via the WebSocket API instead of relying solely on the batched Reporting API. You can filter for sip_trunk_state_change events and correlate the timestamps of the failed state with the active state on the secondary trunk. Here is a quick Java SDK snippet to help you subscribe to these events:
WebSocketClient client = new WebSocketClient();
client.subscribe("/v2/analytics/events/sip", event -> {
if (event.getEventType().equals("sip_trunk_state_change")) {
System.out.println("Trunk: " + event.getEntityId() +
" State: " + event.getState() +
" Timestamp: " + event.getTimestamp());
}
});
This approach bypasses the aggregation delay. Keep in mind that under high concurrency, the event stream itself might experience backpressure, so monitor your WebSocket connection limits closely. If you are pushing significant volume, consider implementing a local buffer to handle bursts of state changes without dropping events. This will give you a clearer view of the actual failover speed versus the reported average.
Take a look at at the reporting bucket configuration rather than assuming the SIP logs are the sole source of truth. In Zendesk, we were used to event-based logging that captured every micro-interaction, but Genesys Cloud aggregates metrics into fixed time windows by default. The 4-second average you see likely aligns with the standard 5-second reporting bucket, which smooths out the sub-second rerouting events. This isn’t necessarily a bug, but a difference in how the platform handles metric granularity compared to the raw SIP transaction lifecycle.
To get a clearer picture that matches your carrier logs, try querying the sip_trunk_failover_duration with a smaller aggregation interval if the API allows, or cross-reference with the sip_trunk_408_count metric to isolate the timeout events. A common fix in migration projects is to build a custom dashboard that overlays the raw 408 counts against the aggregated failover duration. This helps visualize the discrepancy without relying on a single metric. Also, check if your BYOC trunk settings in the Singapore region have any specific latency configurations that might affect how the reporting engine calculates the start and end times of the failover event.
{
"metric_name": "sip_trunk_failover_duration",
"aggregation": "AVG",
"interval": "5s",
"filters": {
"region": "ap-southeast-1"
}
}
Adjusting the interval or using a different metric combination usually resolves the confusion. It’s a steep learning curve coming from Zendesk’s simpler reporting model, but once you map the metrics correctly, the data becomes much more reliable.