Measure CPU Utilization of Cloud Based Architecture

To measure CPU utilization in a cloud-based architecture, or, to be specific, in a cluster (e.g., a Kubernetes cluster, Hadoop cluster, or any distributed system), you need to monitor CPU usage across all nodes in the cluster. Some people might be unaware of the cluster. A cluster is a collection of servers (virtual or physical) grouped to perform a specific set of tasks, share workloads, or provide redundancy in cloud environments to achieve high availability, scalability, and performance.

Why is the CPU measure approach different?

Aspect	Physical Machine	Cluster
Scope	One machine	Many machines (nodes)
CPU Count	Fixed number of cores	Sum of all cores in all nodes
Metrics Source	OS-level tools (`top`, `htop`, `sar`)	Metrics aggregated across nodes (Prometheus, CloudWatch, etc.)
Usage Context	Measures actual hardware usage	Measures usage per node, pod, container, or application
Workload Scheduling	Manual or OS scheduler	Cluster scheduler (e.g., Kubernetes) distributes workloads
Isolation	All processes share same CPU	Containers/VMs may be CPU-isolated or limited per quota
Units	% of one machine’s CPU	% per node, or millicores per pod (in K8s)

Considering the points discussed, we have established the differences in measurement approaches and metrics; we will now examine the key metrics used to evaluate CPU utilization in a cluster-based architecture.

CPU Metrics

Metric	Description
CPU Utilization (%)	Percentage of CPU capacity being used on each node and across the cluster.
CPU Requests vs Limits (Kubernetes)	CPU resources requested vs maximum allowed per pod/container.
CPU Throttling	Amount of time containers are throttled due to hitting CPU limits.
CPU Core Saturation	Consistent high utilization on specific cores.
CPU Usage by Pod/Process	CPU used by each container, pod, or process.

How to measure?

Some of the impor

Kubernetes Cluster:
- Tools:
  - kubectl
  - Prometheus + Grafana
  - Cloud-native dashboards
- Commands:
  - kubectl top nodes # CPU usage per node
  - kubectl top pods # CPU usage per pod
- PromQL Query (Prometheus):
  - 100 – (avg by (instance) (irate(node_cpu_seconds_total{mode=”idle”}[5m])) * 100)
VM or Bare-Metal Cluster:
- Tools/Utilities:
  - top
  - htop
  - sar
  - Prometheus (with node_exporter)
  - Zabbix
- Commands:
  - mpstat -P ALL 1 5 # Per-CPU usage over 5 seconds
AWS Cloud Environment:
- Tool:
  - CloudWatch
- Command:
  - aws cloudwatch get-metric-statistics –metric-name CPUUtilization \–namespace AWS/EC2 –statistics Average –period 300 –start-time …
Azure Cloud Environment:
- Tool:
  - Azure Monitor
  - For AKS – Enable Container Insights via Azure Monitor
Google Cloud Platform:
- Tool:
  - Cloud Monitoring

Common Tools:

Datadog
New Relic
Dynatrace
Grafana Cloud
Zabbix / Nagios

Common Bottlenecks:

Cause	Description
Overloaded Nodes	Some nodes run hot while others are idle, due to poor scheduling or imbalance.
Insufficient CPU Resources	The total CPU capacity is too low for the workload demand.
Noisy Neighbors	In multi-tenant clusters, one workload consumes excessive CPU, starving others.
Improper Resource Requests/Limits	In Kubernetes, if limits are too low or not defined, containers may be throttled or over-provisioned.
Missing Auto-Scaling	Workloads scale up but the infrastructure does not (or slowly).
Long-Running CPU-Bound Tasks	Jobs that max out CPU continuously can saturate cluster resources.

Additional Information

Generic Formula:
- Total Cluster CPU Utilization (%) = (Sum of CPU usage across all nodes) / (Total available CPU cores * 100) * 100
Monitor both real-time and historical trends.
Set alerts for thresholds (e.g., CPU > 80% for 5 mins).
Analyze per-node and cluster-wide.
Combine CPU metrics with memory and disk I/O for full visibility.
Use horizontal pod autoscaling based on CPU (in Kubernetes).
Implement load balancing and affinity rules wisely.
Regularly audit unused CPU capacity to reduce cost.

Cloud-based architecture or Cluster CPU performance refers to how efficiently and effectively the combined CPU resources of all nodes in a cluster are utilized to run workloads. It is a key indicator of a cluster’s capacity to handle concurrent processing, scalability, and responsiveness under load.