Autoscaling
Autoscaling is the automated adjustment of compute resources — adding or removing instances, containers, or pods — in response to real-time demand, load, or defined schedules, so that capacity tracks traffic without manual intervention.
18 resources across 3 libraries
Glossary Terms(5)
Autoscaling
Autoscaling is the automated adjustment of compute resources — adding or removing instances, containers, or pods — in response to real-time demand, load, or de…
Cold Start
A cold start is the added latency incurred when a serverless function, container, or newly-scaled instance must be initialized from scratch — provisioning reso…
Availability Zone
An availability zone (AZ) is one or more physically separate data centers within a cloud region, each with independent power, cooling, and networking, designed…
Cloud Load Balancer
A cloud load balancer is a managed service that distributes incoming network traffic across multiple backend instances, containers, or services, improving avai…
SLO
An SLO (Service Level Objective) is a specific, measurable target for a service's reliability — such as '99.9% of requests succeed within 300ms over a rolling…
Study Notes(6)
Instance Groups and Autoscaling
Understand how Managed Instance Groups keep fleets of Compute Engine VMs healthy and scale them automatically based on load.
VM Scale Sets
Azure Virtual Machine Scale Sets let you deploy and manage a group of identical, load-balanced VMs that automatically grow or shrink based on demand.
Cluster Autoscaling
Learn how the Cluster Autoscaler adds and removes worker nodes so Pod-level autoscalers always have capacity to grow into.
Horizontal Pod Autoscaling
Learn how Kubernetes automatically scales the number of Pod replicas in and out based on CPU, memory, and custom metrics.
Vertical Pod Autoscaling
Understand how VPA right-sizes container CPU and memory requests automatically based on observed usage history.
Horizontal Pod Autoscaling
Learn how the HorizontalPodAutoscaler automatically adjusts replica counts based on CPU, memory, or custom metrics to match workload demand.
Interview Questions(7)
What Are the Main Autoscaling Strategies?
Autoscaling automatically adjusts the number of running instances to match demand, and the main strategies are reactive metric-based scaling (CPU, memory, or r…
How Does Horizontal Pod Autoscaling Work in Kubernetes?
The Horizontal Pod Autoscaler (HPA) in Kubernetes automatically increases or decreases the number of pod replicas in a deployment by periodically comparing obs…
What Is Horizontal Pod Autoscaling?
Horizontal Pod Autoscaling (HPA) is a Kubernetes controller that automatically increases or decreases the number of pod replicas in a deployment based on obser…
Horizontal Pod Autoscaler vs Vertical Pod Autoscaler
The Horizontal Pod Autoscaler (HPA) scales a workload out by changing the number of Pod replicas based on observed metrics, while the Vertical Pod Autoscaler (…
How Do You Approach Cloud Cost Optimization?
Cloud cost optimization means continuously matching provisioned capacity to actual demand through rightsizing, committed-use discounts, autoscaling, and elimin…
Horizontal vs Vertical Scaling: What is the Difference?
Vertical scaling (scaling up) adds more CPU, memory, or disk to a single existing machine, while horizontal scaling (scaling out) adds more machines that share…
What is Capacity Planning in DevOps?
Capacity planning is the disciplined practice of forecasting the compute, memory, storage, and network resources a system will need ahead of demand, then provi…