An in‑depth comparison of Kubernetes Horizontal and Vertical Pod Autoscalers—including algorithms, configurations, performance benchmarks, mixed‑mode trade‑offs, custom‑metric integrations, and real‑world case studies—demonstrates how to choose and tune HPA, VPA, and KEDA for rapid traffic spikes while avoiding conflicts.
This guide walks through the problem of fluctuating traffic in Kubernetes, explains the differences between Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA), and provides step‑by‑step commands, YAML examples, best‑practice recommendations, troubleshooting tips, and monitoring alerts for deploying a production‑grade HPA + VPA solution.
Kubernetes 1.33 introduces in‑place vertical pod resizing, allowing administrators to adjust CPU and memory resources on running containers without restarting pods, reducing downtime for stateful workloads, improving cost efficiency, and integrating with VPA, while outlining implementation details, supported runtimes, limitations, and practical demos.
This article explains the fundamentals, components, workflow, configuration, best practices, and comparison with HPA for Kubernetes Vertical Pod Autoscaler (VPA), helping readers efficiently tune pod resources and improve cluster utilization.
This guide explains what Kubernetes VPA is, its architecture, version compatibility, step‑by‑step installation, certificate setup, manifest generation, practical VPA configuration, validation procedures, performance testing, and known limitations, enabling you to optimize pod resources in cloud‑native clusters.
This article summarizes Zhang He’s Bilibili SRE talk on building a capacity‑management system that visualizes resource usage, reduces costs, improves stability, and leverages Kubernetes VPA, HPA, pooling, and quota management to support massive live‑stream events and rapid feature releases.
The article details a Tencent‑led, end‑to‑end cost‑optimization project for large‑scale Kubernetes/TKE clusters that collected extensive workload metrics, applied VPA/HPA enhancements, custom scheduling and node‑downscaling via the open‑source Crane platform, ultimately delivering up to 70% CPU and 50% memory savings with zero‑fault deployments.
This article presents a comprehensive, data‑driven case study of how Tencent’s internal Kubernetes/TKE platform reduced monthly CPU usage by up to 70% and memory usage by 50% through systematic cost data collection, VPA/HPA enhancements, custom scheduling, node‑level over‑commit, and safe node decommissioning, while maintaining zero‑incident reliability.
This article explains how to use Kubernetes native autoscaling components—HPA, VPA, Cluster Autoscaler—and cloud‑native extensions such as Alibaba Cloud's Virtual Node, Elastic Container Instance, Elastic Workload, and the open‑source OpenKruise to build a cost‑effective, highly elastic architecture on ACK clusters.
This article reviews the current state of Kubernetes horizontal and vertical autoscaling, compares HPA, VPA, and KNative KPA, discusses their limitations, and proposes short‑ and long‑term ideas for a more dynamic, low‑ops scheduling system.
This article explains how Kubernetes' built‑in autoscaling mechanisms—Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler—work, when to use each, and best‑practice tips to reduce cloud costs while maintaining application performance.
