Resource utilization
Resource utilization is the amount or proportion of a system's available computing or physical capacity occupied or consumed by a workload.
Amazon Web Services in Action, Third Edition: An in-depth guide to AWS
CPU utilization is the proportion of processor capacity consumed by a system or workload over a measurement interval.
- How it is used
- Uses CPU utilization to monitor cache performance and adjust EC2 instance capacity as workload changes.
- How deep it goes
- Explains thresholds, a Redis host-versus-process measurement trap, and the metric-to-alarm-to-policy path; it does not derive queuing theory.
- Examples
- Shows a WordPress Auto Scaling policy targeting 70% average CPU utilization and a Redis process that can saturate one core.
- What you can do after
- Operate/configure it by monitoring the appropriate CPU metric and setting an EC2 target-tracking policy or CloudWatch alarm for scaling.
Starting Data Analytics with Generative AI and Python
Resource utilization is the amount or proportion of available computing resources consumed by a system or task.
- How it is used
- Measures system consumption alongside execution time and throughput to locate performance limits and emerging issues.
- How deep it goes
- Defines CPU-bound and I/O-bound behavior and gives monitoring guidance, but stops short of comprehensive systems monitoring.
- Examples
- Uses bootstrap confidence intervals, CPU percentages, disk I/O counters, memory readings, and a monitored function.
- What you can do after
- Diagnose whether a function is CPU-bound or I/O-bound and estimate its memory usage through sampled measurements.
Read how Starting Data Analytics with Generative AI and Python explains resource utilization →
Kubernetes for Developers
A CPU metric is a measurement of processor consumption or capacity associated with a workload.
- How it is used
- Uses observed Pod CPU consumption to right-size resource requests and drive automatic replica scaling.
- How deep it goes
- Explains observation, load testing, request adjustment, and HPA behavior, but does not develop CPU measurement internals.
- Examples
- Demonstrates kubectl top, Apache Bench load, CPU-intensive request handling, and worker Pods scaled from two to ten replicas.
- What you can do after
- Configure resource requests and a CPU-based Horizontal Pod Autoscaler, then observe scaling with kubectl.
Read how Kubernetes for Developers explains resource utilization →
Acing the Certified Kubernetes Administrator Exam, Second Edition
Resource utilization is the amount of CPU or memory currently consumed by a node or workload.
- How it is used
- Uses actual CPU and memory consumption to inspect nodes, evaluate workloads, and drive horizontal pod scaling.
- How deep it goes
- Explains kubectl measurements, HPA percentage calculation, and allocation distinctions, but stops short of metrics-server internals.
- Examples
- Uses nginx, kubectl top, kubectl autoscale, CPU requests, and node allocation output as operational examples.
- What you can do after
- Diagnose resource pressure with kubectl top and distinguish current consumption from scheduler-visible allocation.
Microsoft Azure in Action
CPU utilization, or CPU usage, is the proportion of a virtual machine's processor capacity consumed during a measurement interval.
- How it is used
- Monitors virtual-machine health, identifies workload pressure, and supports scaling or code-review decisions.
- How deep it goes
- Explains the metric, related signals, historical comparison, and threshold alerts, but stops short of CPU internals.
- Examples
- Uses percentage-CPU graphs, an 85%-for-10-minutes alert, and a dynamic alert for an unusual rise from 20% to 60%.
- What you can do after
- Diagnose/debug with it by correlating CPU readings with other VM metrics and configuring proactive Monitor alerts.
Read how Microsoft Azure in Action explains resource utilization →
Machine Learning Platform Engineering
Resource utilization is a measurement of the computational resources consumed by a running application, such as CPU, memory, or capacity.
- How it is used
- Monitors deployed API services to maintain performance, guide capacity allocation, and support reliability decisions.
- How deep it goes
- Defines basic CPU, memory, and capacity monitoring, then connects it to Prometheus and Grafana without detailing metric internals.
- Examples
- Uses BentoML deployments, Prometheus scraping, Grafana dashboards, and Kubernetes pod capacity as operational examples.
- What you can do after
- Operate/configure Prometheus and Grafana monitoring for deployed BentoML applications and inspect utilization alongside request health.
Read how Machine Learning Platform Engineering explains resource utilization →
Apache Kafka in Action
CPU utilization is the proportion of processor capacity occupied by executing work over a measurement interval.
- How it is used
- Uses CPU utilization to monitor Kafka broker health, detect processing pressure, and inform scaling decisions.
- How deep it goes
- Explains transient versus sustained utilization and its operational consequences, but does not provide a calculation or collection procedure.
- Examples
- Uses short CPU spikes, prolonged high utilization, partition rebalancing, and physical-hardware monitoring as operational examples.
- What you can do after
- Diagnose/debug with it by distinguishing temporary broker load from sustained pressure that warrants investigation or scaling.
Read how Apache Kafka in Action explains resource utilization →
Kubernetes in Action, Second Edition
Infrastructure utilization is the proportion of available computing resources that are actively used by workloads.
- How it is used
- Uses workload placement and movement to pack applications onto infrastructure and reduce server costs.
- How deep it goes
- Explains placement, movement, and tighter packing, but gives no utilization metrics or calculations.
- Examples
- Uses the scenario of moving applications to make room for a larger deployment and pack nodes tightly.
- What you can do after
- Understand how Kubernetes placement and movement can pack workloads more tightly and run more applications on fewer servers.
Read how Kubernetes in Action, Second Edition explains resource utilization →
Related concepts
- monitoringmeasured by · Monitoring is the ongoing observation and measurement of a system’s behavior, state, or operational signals to assess its condition and detect changes or problems.
- performancekind of · Performance is the measurable effectiveness or operational behavior of a system under specified conditions, assessed through relevant qualities such as task results, speed, responsiveness, throughput, or resource use.
- resource allocationcontrasts with · Resource allocation is the assignment of limited resources among competing activities, workloads, or components according to requirements, priorities, and constraints.
- kubectl topmeasured by · kubectl top is a kubectl command that displays current CPU and memory usage for Kubernetes nodes or pods.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.