System Capacity Planner

Calculate an overall server capacity score from CPU, memory, disk and network utilization, identifying the limiting resource.

Inputs

CPU Utilization%

Memory Utilization%

Disk Utilization%

Network Utilization%

Overall Capacity Score

22.0

Bottleneck Resource

Memory

Bottleneck Utilization

78.0%

Status

Healthy — ample headroom across all resources

Step by step

Values used
CPU Utilization = 65 %; Memory Utilization = 78 %; Disk Utilization = 55 %; Network Utilization = 40 %
Capacity score
score = 100 − max(cpu%, memory%, disk%, network%)
Overall Capacity Score
= 22.0
Bottleneck Resource
= Memory
Bottleneck Utilization
= 78.0
Status
= Healthy — ample headroom across all resources

How it works

A server's overall capacity headroom is only as good as its most constrained resource — even if CPU, disk and network all have plenty of room, a server pinned at 95% memory utilization is effectively out of capacity. This calculator therefore scores overall capacity as 100 minus the highest of the four utilization percentages, and explicitly names that highest-utilization resource as the bottleneck, since that is the one that needs attention (or additional capacity) before any of the others.

Formula

Capacity score

score = 100 − max(cpu%, memory%, disk%, network%)

c: CPU utilization %
m: memory utilization %
d: disk utilization %
n: network utilization %

Frequently Asked Questions

Why use the maximum instead of an average of the four metrics?

Averaging would hide a critical single-resource bottleneck behind three healthy metrics — a server can be functionally out of capacity even with low average utilization if just one resource (commonly memory or disk) is pinned near 100%, so the true constraint is always the highest individual figure.

What should I do once I identify the bottleneck resource?

Address that specific resource first — add RAM or tune memory-hungry services if memory is the bottleneck, expand or clean up storage if disk is the constraint, and so on — since improving non-bottleneck resources provides no additional real-world capacity until the bottleneck itself is resolved.

How often should these utilization percentages be measured?

Use sustained averages over a representative window (an hour or a business day) rather than instantaneous snapshots, since brief spikes are normal and don't necessarily indicate a capacity problem — most monitoring systems track p95 or similar percentiles over rolling windows for exactly this reason.

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