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This guide explains how to use cAdvisor‑provided container network traffic counters as custom metrics for Kubernetes HPA, covering metric collection, Prometheus‑adapter configuration, verification, and a complete HPA testing workflow for elastic scaling of non‑CPU‑intensive workloads.
This guide explains what Kubernetes Horizontal Pod Autoscaler (HPA) is, how it works, its key features, and provides step‑by‑step configuration, verification, and scaling policy details with practical code examples for cloud‑native applications.
Vivo’s container platform combats oversized resource requests by first applying static coefficient‑based overcommit at deployment and then using a dynamic recommender that continuously gathers usage metrics, builds exponential histograms with a half‑life sliding‑window model, and adjusts CPU (and optionally memory) requests, improving packing efficiency, reducing billing, and boosting CPU utilization by up to eight percent while maintaining HPA accuracy.
The QQ image storage platform was fully migrated from VM‑based servers to Tencent Cloud’s Kubernetes Engine, consolidating services into containers, adding health checks, anti‑affinity, and autoscaling, which cut costs by 26%, reduced ops effort 30%, and improved scalability and reliability.
This article explores Kubernetes horizontal pod autoscaling mechanisms, comparing HPA and KEDA, their implementation principles, limitations, and practical deployment scenarios for cloud-native applications.
This article explains pod‑level elasticity in Kubernetes by detailing the principles, metric types, and limitations of the Horizontal Pod Autoscaler (HPA) and then introduces KEDA as an event‑driven extension that adds true scale‑to‑zero capabilities, complete with configuration examples and code snippets.
The article explains Kubernetes’ built‑in Horizontal Pod Autoscaler, then details the custom Water Pod Autoscaler (WPA) that extends HPA with dual‑signal (load and SOA registration) detection, dual‑threshold scaling, noise filtering, configurable cooldown, frequency limits, tolerance buffers, and integrated alerting for reliable elastic scaling.
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 explains three frequent misunderstandings about Kubernetes Horizontal Pod Autoscaler—dead zones, misuse of utilization calculations, and perceived lag in scaling—while detailing HPA’s inner workings, metric sources, calculation methods, and behavior configuration to help you avoid scaling pitfalls.
This article explains the background, benefits, and measurement criteria of Kubernetes Horizontal‑Pod‑Autoscaler (HPA), describes the creation of metric tables and SQL queries for collecting scaling events and CPU usage, and presents a Python‑based workflow that aggregates the data, stores daily reports, validates results, and sends automated email summaries.