Deploying API implementations to CloudHub11% of the exam
CloudHub Worker Sizing and Configuration
Size classic CloudHub workers by sustained compute, heap and disk, then plan bounded autoscaling against available account capacity.
Select CloudHub worker sizes and configuration as appropriate.
~7 min read · 6 practice questions
What the exam expects
- Separate worker size from worker count and account entitlement.
- Read heap, worker memory and disk as different capacities.
- Select sustained compute capacity rather than relying on startup bursts.
- Plan classic CloudHub autoscaling within its limits, evaluation periods and available resources.
A worker is an independently managed Mule runtime instance; a vCore expresses configured compute capacity; heap is the Java object-memory area; scale-up changes worker size; scale-out changes worker count; an autoscaling policy adjusts resources in response to sustained usage (Architecture, Autoscaling). These are classic worker-based CloudHub controls, rather than CloudHub 2 replica settings.
Sources for this section (2)
Key facts
Fractional compute
- Detail
- 0.1- and 0.2-vCore workers offer limited capacity with short bursts.
Consistent compute
- Detail
- Workers with 1 or more vCores provide performance consistency.
Heap comparison
- Detail
- 1 vCore has 2 GB heap; 2 vCores has 4 GB heap.
Disk comparison
- Detail
- 1, 2 and 4 vCores provide 12, 40 and 88 GB disk respectively.
Disk scope
- Detail
- Allocation includes operating-system and application storage.
Policy ceiling
- Detail
- Autoscaling supports at most four workers and 16-vCore size.
Scaling delay
- Detail
- Each step involves evaluation time plus cool-down.
CPU scope
- Detail
- Overall worker CPU, not JVM CPU alone.
Sources for this section (2)
- Why does the initial 128 MB metaspace size not establish its maximum?
- When available vCores are insufficient, why can an application exist without starting?
- When investigating earlier autoscaling events, where can the configured alert's history be inspected?
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