This guide walks through configuring Kubernetes Horizontal Pod Autoscaler, covering prerequisite checks, proper CPU request settings, creating CPU‑based and custom‑metric HPA objects, stability tuning, rollout interactions, observation techniques, and risk‑aware acceptance and rollback procedures.
The article explains how to replace CPU‑only autoscaling for large‑model inference services with a Kubernetes HPA that scales pods according to a custom queue‑depth metric exported to Prometheus, covering metric definition, deployment configuration, Prometheus‑Adapter setup, HPA creation, capacity calculation, validation, troubleshooting, and rollback procedures.
This guide walks you through a complete, 30‑minute implementation of Kubernetes node autoscaling using Horizontal Pod Autoscaler (HPA) with custom Prometheus metrics, covering prerequisites, anti‑pattern warnings, environment matrix, step‑by‑step deployment, core principles, observability, troubleshooting, best practices, and FAQ.
Locust provides a built‑in web UI for real‑time monitoring of metrics such as RPS, average response time, failures, and active users, supports headless CSV export for deeper analysis, and allows custom statistics via event listeners in your test scripts, enabling comprehensive user access reporting.
This guide explains how to leverage Locust's built‑in statistics and reporting features to monitor HTTP requests in real time via its web UI, export results as CSV files, customize metric collection in scripts, and analyze the data with external tools.
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 article explains how the Picasso platform provides a standardized, high‑efficiency A/B testing pipeline for front‑end developers, covering traffic allocation algorithms, flow reuse, orthogonal and mutually exclusive experiments, multi‑scenario rules, custom metrics, reporting, and future enhancements.
This guide explains how to configure node_exporter’s textfile collector to expose custom metrics, write them safely via scripts and cron, and monitor them with Prometheus, including examples of metric formatting, atomic file updates, and directory size collection.
This guide walks through setting up Kubernetes Horizontal Pod Autoscaling (HPA) using the Metrics Server for CPU and memory metrics, deploying a sample Go web app, performing load tests, and extending autoscaling with Prometheus‑based custom metrics for fine‑grained scaling control.
This guide walks through deploying an Nginx sample app on Kubernetes, exposing Prometheus‑collected custom metrics, configuring a Prometheus adapter, and creating a Horizontal Pod Autoscaler that scales the deployment based on request‑per‑second metrics.
This guide walks you through configuring Kubernetes Horizontal Pod Autoscaler using both autoscaling/v1 (CPU‑only) and autoscaling/v2beta1 (custom metrics), covering template creation, deployment, Metrics Server migration, custom metrics adapter setup, load testing, and verification of scaling behavior.
This article explains how to configure Kubernetes Horizontal Pod Autoscaler (HPA) to scale workloads based on custom business metrics collected by Prometheus, covering installation of Metrics Server, deployment of a demo app, setup of the Prometheus adapter, and practical load‑testing steps.
