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Answer: Change the filter to metric.label.state = 'used' AND metric.label.state = 'buffered' AND metric.label.state = 'cached' AND metric.label.state = 'slab'.
The correct answer is C. The current filter only considers memory in the 'used' state. However, the operating system also uses memory for other purposes like caching, buffering, and slab usage. By modifying the filter to include 'used', 'buffered', 'cached', and 'slab', we ensure the autoscaling policy considers all memory states, providing a more accurate representation of total memory usage. This adjustment will help the autoscaler make better decisions based on the actual memory usage and ensure scaling under high load.
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You have a Compute Engine application deployed in Google Cloud Platform (GCP) that you want to autoscale when total memory usage exceeds 80%. As part of the configuration, you have installed the Cloud Monitoring agent on the instances and set up an autoscaling policy with the following details:
However, during periods of high load, you notice that the application does not scale as expected. What should you do to ensure the application scales properly?
A
Change the Target type to DELTA_PER_MINUTE.
B
Change the Metric identifier to agent.googleapis.com/memory/bytes_used.
C
Change the filter to metric.label.state = 'used' AND metric.label.state = 'buffered' AND metric.label.state = 'cached' AND metric.label.state = 'slab'.
D
Change the filter to metric.label.state = 'free' and the Target utilization to 20.