Predictive Routing Queue Depth Spike with JMeter Load

Genesys Cloud Routing
1

Trying to understand why our BYOC trunks are mapping SIP 408 Request Timeout events to ‘No Answer’ instead of a specific disposition. We see a significant discrepancy in the Architect flow analytics versus the WFM adherence reports. When running a JMeter load test simulating 500 concurrent agents, the predictive routing queue depth spikes artificially, causing the algorithm to stop offering calls to agents marked as ‘Available’ in the schedule. The /api/v2/wfm/scheduling/v2/schedules endpoint returns successful publish confirmations, yet the real-time status in the WFM dashboard lags by nearly 90 seconds during these load windows. This delay prevents the routing engine from accurately calculating the available workforce, leading to a cascade of abandoned calls that are misclassified in the post-call analytics. We are using the v2 SDK in Python to sync shift updates, but the conflict resolution for overlapping time-off requests seems to exacerbate the latency. The issue is most pronounced during our central time zone morning rush, specifically between 08:00 and 09:30 CST. Is there a known threshold for agent status sync delays that triggers this predictive routing behavior, or should we be adjusting the queue depth calculation parameters in the routing configuration?

2

Adjust the JMeter HTTP Request sampler timeout values. The default 30-second timeout often triggers the SIP 408 before the platform can process the initial offer, leading to the “No Answer” disposition mismatch.

<elementProp name="HTTPSampler.connect_timeout" elementType="String">
 <stringProp name="Value">60000</stringProp>
</elementProp>
<elementProp name="HTTPSampler.response_timeout" elementType="String">
 <stringProp name="Value">60000</stringProp>
</elementProp>

The discrepancy between Architect analytics and WFM adherence usually stems from how the platform interprets these timed-out sessions. When the client hangs up before the server responds, the system logs it as a rejected or missed call rather than a true non-answer.

[WARN] SIP/2.0 408 Request Timeout for Call-ID:

Increasing the timeout allows the predictive routing engine to properly register the call attempt. This ensures the queue depth calculation remains accurate during high-concurrency simulations. For digital channel exports, maintaining this data integrity is crucial for audit trails. Check the Retry-After headers if you still see rate limiting issues after this change.

3

Make sure you verify the actual WebSocket connection limits before pushing 500 concurrent agents. The previous suggestion about HTTP timeouts is valid for the API layer, but it misses the core issue here: the predictive routing engine relies on real-time WebSocket heartbeats for agent state. If JMeter is hammering the /api/v2/wfm/scheduling endpoint, it might be saturating the available WebSocket connections for that tenant, causing the platform to drop state updates. This leads to the “artificial” queue depth spike because the system thinks agents are offline or busy when they are actually available.

A common fix in load testing environments involves separating the control plane traffic from the signaling plane. Here is how to adjust the JMeter thread group:

  • Limit API Throughput: Cap the WFM scheduling API calls to 10 req/s. Use the Constant Throughput Timer to prevent rate-limiting (429s) which can cascade into state desyncs.
  • Simulate Heartbeats: Ensure your JMeter script includes periodic WebSocket keep-alive messages. Without these, the platform times out the agent session after 30 seconds of inactivity, marking them as unavailable.
  • Check Connection Pool: In the HTTP Request Defaults, set Connection Timeout to 10000 and Response Timeout to 30000. Do not exceed these significantly as it may hold open resources on the Genesys Cloud side.
{
 "jmeter_config": {
 "throughput_timer": {
 "target_rps": 10,
 "calc_mode": "threads"
 },
 "websocket": {
 "heartbeat_interval_ms": 15000,
 "max_connections": 500
 }
 }
}

If the queue depth still spikes, check the x-ratelimit-remaining headers. If they hit zero, the predictive dialer stops offering calls to protect the trunk capacity. This is a capacity ceiling, not a bug. Reduce the concurrent agent count in the test until the rate limit headers remain stable.