7.4

Table 4‑1. Data Collection Default Intervals
Data Collection Type Default Interval
Inventory Every 24 hours (daily)
State Every 15 minutes
Performance Every 24 hours (daily)
Performance Analysis and Tuning
As the number of resources collecting data increases, data collection completion times might become
longer than the interval between data collection intervals, particularly for state data collection. To
determine whether data collection for a compute resource or endpoint is completing in time or is being
queued, see the Data Collection page. The Last Completed field value might show In queue or In
progress instead of a timestamp when data collection last finished. If this problem occurs, you can
increase the interval between data collections to decrease the data collection frequency.
Alternatively, you can increase the concurrent data collection limit per agent. By default,
vRealize Automation limits concurrent data collection activities to two per agent and queues requests that
exceed this limit. This limitation allows data collection activities to finish quickly without affecting overall
performance. You can raise the limit to take advantage of concurrent data collection, but you must weigh
this option against overall performance degradation.
If you increase the configured vRealize Automation per-agent limit, you might want to increase one or
more of these execution timeout intervals. For more information about how to configure data collection
concurrency and timeout intervals, see the vRealize Automation System Administration documentation.
Manager Service data collection is CPU-intensive. Increasing the processing power of the Manager
Service host can decrease the time required for overall data collection.
Data collection for Amazon Elastic Compute Cloud (Amazon AWS), in particular, can be CPU intensive,
especially if your system collects data on multiple regions concurrently and if data was not previously
collected on those regions. This type of data collection can cause an overall degradation in Web site
performance. Decrease the frequency of Amazon AWS inventory data collection if it is having a
noticeable effect on performance.
Workflow Processing Scalability
The average workflow processing time, from when the DEM Orchestrator starts preprocessing the
workflow to when the workflow finishes executing, increases with the number of concurrent workflows.
Workflow volume is a function of the amount of vRealize Automation activity, including machine requests
and some data collection activities.
This chapter includes the following topics:
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Configure Manager Service for High Data Volume
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Distributed Execution Manager Performance Analysis and Tuning
Reference Architecture
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