3 Ways to get Capacity
Utilization Wrong
Jason Simpson
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Capacity Utilization Metric
• A metric used to
measure the rate at
which potential output
levels are being met or
used
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Capacity Utilization Rate
• Usually expressed as
a percentage, it is
computed by dividing
the total capacity with
the portion being
utilized
100
50
0
75 units / 100 units capacity = .75 (75%) Utilization
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So that is easy…
• Now just monitor the CPU
metric to understand the
capacity utilization
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So how did I get it wrong?
• Most times a single
metric will not give
the full picture for
capacity
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Need to find more metrics…
• What key metrics make up
the capacity utilization
CPU, Memory, I/O, Network Speed, Queue Length, etc.
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Multiple metrics and still not right…
• Understanding the
high water mark for
metrics is essential
when calculating
capacity
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How do we find total capacity…
• Some times it is as simple
as pulling attributes (or
metadata) about the metric
and other times the metric
will need to be observed
4 GB RAM, 45 TPM
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Don’t toss in the towel…
• Applying best
practices and subject
matter expertise are
required
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What is this computed metric…
• Creating computed metrics
is required in most
capacity utilization
measurements
(X IOPS/sec / 300 total IOPS/sec ) = Y (%) Utilization
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Takes some trial and error…
• Unfortunately for most
complex systems it will
take some tuning to
get correct You tried your best
and you failed
miserably.
The lesson
is, never try
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Capacity falls into two buckets…
• Macro trend –
expected capacity
• Micro trends –
unexpected capacity
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Planning verse real time…
• Capacity planning is for
macro trends and real time
monitoring addresses
micro trends
Performance and capacity meet
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Applying best practices…
• Create capacity utilization
metrics from industry
practices
(Z Run Queue / X CPU#)*100 = Y (%) Utilization
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Don’t fall into old habits…
• Over provisioning
infrastructure, applications,
and services
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Leveraging tools…
• Automation is a goal for DevOps
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Getting automation correct…
• Tying capacity utilization to
automation helps to ensure
systems have been
provisioned correctly
My system auto-provisioned 36 instances but
should I leave them running ?
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Icing on the cake…
• Behavior learning
technologies coupled with
metric correlation can assist
with building accurate
capacity utilization metrics
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Putting it all together…
• Example capacity utilization for
Amazon Web Services (AWS)
Infrastructure as a Service (IaaS)
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Recap top 3 items…
1. Many times a single
metric will not give the full
picture for capacity
2. Understanding the high
water mark for the
metrics is essential when
calculating capacity
3. Applying best practices
and subject matter
expertise are required