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Kubernetes Autoscaling Components - Efficient Resource Management
This project provides various components for automatically scaling applications and clusters within Kubernetes, optimizing resource utilization and ensuring performance under varying loads.
Go
Added on 2025年5月22日8,405
Stars4,110
ForksGo
LanguageProject Introduction
Summary
The Kubernetes Autoscaler project includes several core components designed to automatically adjust the number of running pods (Horizontal Pod Autoscaler), the resources allocated to pods (Vertical Pod Autoscaler), and the number of nodes in a cluster (Cluster Autoscaler) based on demand.
Problem Solved
Managing resource allocation manually in dynamic environments is complex, leads to over-provisioning and waste, or under-provisioning causing performance issues. Autoscaling solves this by automatically matching resource supply to application demand.
Core Features
Horizontal Pod Autoscaler (HPA)
Automatically scales the number of pods in a deployment or replicaset based on observed CPU utilization, memory usage, or custom metrics.
Cluster Autoscaler
Automatically adjusts the number of nodes in your Kubernetes cluster, adding nodes when pods are pending due to insufficient resources and removing nodes when they are underutilized.
Vertical Pod Autoscaler (VPA)
Automatically adjusts the CPU and memory requests and limits for containers in a pod based on historical and current usage, allowing for more efficient resource allocation.
Tech Stack
Go
Kubernetes APIs
Prometheus (for metrics)
Cloud Provider APIs (AWS, GCE, Azure, etc.)
使用场景
Kubernetes autoscaling is essential for optimizing cost and performance in dynamic cloud-native environments. Key use cases include:
Handling Variable Traffic Loads
Details
Automatically scale web applications or APIs up during peak hours and down during off-peak hours based on request rate or CPU load.
User Value
Ensures application responsiveness during traffic spikes while minimizing infrastructure costs during low usage periods.
Cost Optimization in Cloud Environments
Details
Dynamically adjust cluster size based on the actual workload, preventing over-provisioning of nodes and saving cloud infrastructure costs.
User Value
Reduces cloud spending by only paying for the resources actively needed by applications.
Optimizing Resource Requests and Limits
Details
Automatically recommend or set optimal CPU and memory requests/limits for applications, improving node utilization and reducing resource waste.
User Value
Increases the density of pods per node and improves overall cluster efficiency.
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