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Status parameters of Adaptive Decision Manager nodes

Updated on March 11, 2021

This content applies only to On-premises and Client-managed cloud environments

The Adaptive Decision Manager (ADM) creates and updates adaptive models by processing customer responses in real time. By including adaptive models in your next-best-actions strategies, you can make better decisions for your business based on accurately predicted customer behavior. Use the following reference information to better understand the status parameters of ADM nodes.

For information on how to access the parameters of a selected node, see Monitoring decision management services.

Node ID
The identification number of the node in the cluster.
# Models updated
The number of models that have been updated since the node was started.
# Models updating
The number of ADM models that are being currently updated.
# Models waiting update
The number of models in the model update queue.
Average waiting time (s)
The average time a model waits in the model update queue since the node was started.
Median waiting time (s)
The median time a model waits in the model update queue since the node was started. This value is more robust to outlier models than the average waiting time.
P95 (s)
For 95 percent of models updated since the node was started, the waiting time in the model update queue was equal to or less than the value of this parameter.
Note: The P95 and P99 values give a summary of the underlying distribution of models. The values identify if there is any significant tail in the waiting times before models are updated. If you observe long waiting times, you can adjust the frequency for updating models or add more nodes.
P99 (s)
For 99 percent of the models updated since the node was started, the waiting time in the model update queue was equal to or less than the value of this parameter.
Note: The P95 and P99 values give a summary of the underlying distribution of models. The values identify if there is any significant tail in the waiting times before models are updated. If you observe long waiting times, you can adjust the frequency for updating models or add more nodes.

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