Autoscaling
Autoscaling is the automatic adjustment of computing resource capacity or running instances in response to workload demand or measured system conditions.
Kubernetes for Developers
Cluster autoscaling is the automated adjustment of cluster node capacity to provide resources for scheduled workloads.
- How it is used
- Adds provider-managed node capacity when Pods need space, including during workload scaling or a new deployment.
- How deep it goes
- Explains provider setup choices, node-type limits, startup delay and a placeholder-Pod remedy; defers uncovered scheduling cases to provider documentation.
- Examples
- Uses GKE Autopilot and node pools for setup choices, then a low-priority placeholder Deployment to demonstrate spare capacity.
- What you can do after
- Operate/configure it through provider-supported autoscaling, and reserve capacity with placeholder Pods when startup delay matters.
Microsoft Azure in Action
The act of decreasing a virtual machine's size to provide fewer computing resources.
- How it is used
- Matches database throughput or compute capacity to changing traffic while limiting idle capacity during quieter periods.
- How deep it goes
- Explains scaling directions, thresholds, instance limits, and Cosmos DB throughput caps; does not detail the underlying scaling mechanism.
- Examples
- Uses holiday shopping traffic, a Cosmos DB Azure CLI command, and database demand during major sales.
- What you can do after
- Operate/configure it by setting Cosmos DB autoscale and an explicit maximum throughput, while accounting for possible cost spikes.
MongoDB 8.0 in Action, Third Edition
Scaling down is reducing a deployment's provisioned cluster tier or resource capacity.
- How it is used
- Manages Atlas cluster tiers and storage capacity in response to workload use while controlling operating costs.
- How deep it goes
- Explains configuration ranges, CPU and memory triggers, cluster-class limits, defaults, and rolling operation, but not custom implementation scripts.
- Examples
- Uses an M30-to-M50 configuration, a memory-use formula, and differing UI, API, and CLI defaults.
- What you can do after
- Operate/configure Atlas autoscaling by setting bounded cluster tiers, storage behavior, and permission to scale down.
Read how MongoDB 8.0 in Action, Third Edition explains autoscaling →
Acing the Certified Kubernetes Administrator Exam, Second Edition
Autoscaling automatically adjusts workload or resource capacity in response to demand or measured conditions.
- How it is used
- Deploys autoscaling as a Kubernetes workload-management task supporting application availability.
- How deep it goes
- Identifies autoscaling as a configuration competency but does not provide implementation commands, manifests, algorithms, or verification procedures.
- Examples
- Uses a competency table that maps configuring workload autoscaling to the chapter’s workload operations.
- What you can do after
- Operate/configure workload autoscaling as a Kubernetes administration and CKA exam task.
Read how Acing the Certified Kubernetes Administrator Exam, Second Edition explains autoscaling →
Google Anthos in Action
Scaling is the adjustment of computing resources or system capacity to handle changing workload demand.
- How it is used
- Deploys serverless workloads and manages cluster capacity in response to demand, resource requirements, or operational constraints.
- How deep it goes
- Explains request-based scaling, scale-to-zero, tuning parameters, and cluster trade-offs without detailing autoscaler internals.
- Examples
- Uses Cloud Run request scaling, Knative instance limits, Anthos on Azure cluster autoscaling, and horizontal or vertical cluster layouts.
- What you can do after
- Operate or configure request-driven services by selecting concurrency and instance bounds while evaluating broader cluster scaling arrangements.
LLMs in Production
Scaling down is the deliberate reduction of active service capacity or computing resources.
- How it is used
- Manages cluster capacity for deployment demand while limiting available resources and avoiding unnecessary fixed infrastructure.
- How deep it goes
- Explains capacity limits and redeployment risks, but stops short of detailing scale-down algorithms or controller behavior.
- Examples
- Uses cloud cluster commands and a configuration listing that caps CPU, memory, T4, A100, and A100 80GB resources.
- What you can do after
- Operate/configure node auto-provisioning with explicit resource caps and treat LLM capacity reductions as potentially slow to reverse.
Bootstrapping Microservices, Second Edition: With Docker, Kubernetes, GitHub Actions, and Terraform
Elastic scaling is the automatic expansion and contraction of a service's running capacity in response to changing workload.
- How it is used
- Automates cluster and microservice capacity changes to match variable demand while reducing unnecessary resource costs.
- How deep it goes
- Explains cluster and replica bounds with Terraform and Kubernetes resources, while deferring detailed autoscaler customization to documentation.
- Examples
- Uses Terraform node-pool settings and an autoscaling/v2 gateway HorizontalPodAutoscaler with minimum and maximum replica counts.
- What you can do after
- Operate/configure Kubernetes autoscaling for cluster nodes and individual microservice replicas.
Related concepts
- HorizontalPodAutoscalerimplemented by · A HorizontalPodAutoscaler is a Kubernetes API resource and controller that adjusts a workload’s replica count according to observed metrics and configured targets.
- horizontal scalingincludes · Horizontal scaling is adjusting system capacity by adding or removing computing nodes or equivalent instances that share or distribute the workload.
- vertical scalingincludes · Vertical scaling is increasing or decreasing a system’s capacity by changing the resources available to an existing machine, node, or instance.
- Kubernetesused by · Kubernetes is an open-source platform for orchestrating and automating the deployment, scaling, and management of containerized workloads across computing clusters.
- costproduces · Cost is the monetary expenditure, computational or other resource consumption, effort, or burden incurred in acquiring, producing, operating, or performing something.
- computer clusterconfigures · A computer cluster is a group of interconnected computing nodes coordinated to provide shared computing resources, execute workloads, or deliver services.
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