CV0-003 · Question #31
A cloud engineer notices on a dashboard that the host is close to reaching maximum capacity for the CPU and memory in the cloud environment, which could cause performance issues. The cloud…
The correct answer is B. Adjust the cloud workload by migrating resource-intensive applications to different hosts. When a host approaches CPU and memory capacity limits, migrating resource-intensive workloads to other hosts with available headroom redistributes load across existing infrastructure without adding hardware.
Question
A cloud engineer notices on a dashboard that the host is close to reaching maximum capacity for the CPU and memory in the cloud environment, which could cause performance issues. The cloud environment has 100 servers, with 25% of the servers consuming their compute only during peak business hours, 25% consuming half of the allocated resources, and the remaining 50% using the compute during off hours. Which of the following should the engineer perform to optimize the efficiency of the compute usage in the cloud?
Options
- AAdd additional CPUs and RAM to the host that is serving the cloud.
- BAdjust the cloud workload by migrating resource-intensive applications to different hosts.
- CAdd additional hosts to the environment using the cloud management tool.
- DEnable automatic scaling in the cloud management tool.
How the community answered
(34 responses)- A21% (7)
- B62% (21)
- C6% (2)
- D12% (4)
Why each option
When a host approaches CPU and memory capacity limits, migrating resource-intensive workloads to other hosts with available headroom redistributes load across existing infrastructure without adding hardware.
Adding CPUs and RAM is vertical scaling that increases hardware cost without addressing the uneven workload distribution already present across the 100-server environment.
Migrating resource-intensive applications from the overloaded host to underutilized hosts optimizes compute efficiency across the existing pool. The scenario reveals that 25% of servers only run during peak hours and another 25% use only half their allocated resources, meaning significant headroom already exists on those hosts to absorb migrated workloads - making redistribution the most efficient solution before resorting to adding capacity.
Adding new hosts is horizontal scaling that increases infrastructure footprint rather than optimizing utilization of currently underused existing servers.
Automatic scaling dynamically provisions or de-provisions resources in response to demand but does not rebalance existing workloads across hosts that already have available capacity.
Concept tested: Cloud workload migration for compute load balancing
Source: https://docs.vmware.com/en/VMware-vSphere/8.0/vsphere-resource-management/GUID-517C3B89-3BCA-414D-B935-78F4A84CBFB8.html
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