DaemonSet Capacity Calculator
Calculate the total CPU and memory a DaemonSet consumes across every node in the cluster, since one pod always runs per node.
Inputs
Total Cluster CPU (DaemonSet)
2.00cores
Total Cluster Memory (DaemonSet)
2.50GiB
Total CPU
2,000millicores
Total Memory
2,560MiB
Step by step
Total CPU = nodes × per-pod CPU
20 × 100m
= 2000m (2.00 cores)
Total memory = nodes × per-pod memory
20 × 128MiB
= 2560MiB (2.50 GiB)
How it works
A DaemonSet always schedules exactly one pod per eligible node, so its total resource footprint scales linearly with cluster size rather than with traffic or workload demand. This makes DaemonSets (CNI plugins, log shippers, node monitoring agents) a fixed tax on every node's capacity — one that grows automatically whenever the cluster autoscaler adds nodes, and should be netted out before sizing pod density for regular workloads.
Formulas
Total DaemonSet CPU
totalCPU = nodeCount × perPodCPU
- N
- Number of nodes in the cluster
- C_p
- Per-pod CPU request in millicores
- T_c
- Total DaemonSet CPU across cluster in millicores
Total DaemonSet memory
totalMemory = nodeCount × perPodMemory
- N
- Number of nodes in the cluster
- M_p
- Per-pod memory request in MiB
- T_m
- Total DaemonSet memory across cluster in MiB
Frequently Asked Questions
Why does DaemonSet resource usage scale with nodes, not pods?
By design a DaemonSet controller ensures exactly one copy of the pod runs on every node matching its selector — adding more nodes automatically adds more DaemonSet pods, regardless of application-level traffic or replica counts.
How many DaemonSets does a typical cluster run?
Commonly 3-6: a CNI agent, kube-proxy, a log shipper (Fluent Bit/Fluentd), a node-level metrics exporter (node-exporter), and sometimes a security or service mesh agent.
How should I account for this when sizing regular workload capacity?
Subtract the per-node DaemonSet CPU/memory footprint from allocatable node capacity before running the pod density calculator for your application pods, since that capacity is unavailable regardless of scheduling.