0 views collected around this technical thread.
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.
This article details how a solo engineer built and operated a SaaS platform on AWS using Kubernetes, covering infrastructure overview, automatic DNS, TLS, load balancing, CI/CD rollouts, autoscaling, caching, secret management, monitoring, logging, error tracking, and cost‑effective operations.
This guide explains how to quickly build a high‑performance, observable, and elastically scalable LLM inference service by deploying NVIDIA NIM on an Alibaba Cloud ACK cluster using the Cloud‑Native AI Suite, KServe, Prometheus, Grafana, and custom autoscaling based on request‑queue metrics.
This guide explains how to efficiently deploy AI model inference services on Knative by externalizing model data, using Fluid for accelerated loading, configuring secrets, ImageCache, graceful shutdown, probes, autoscaling parameters, mixed ECS/ECI resources, shared GPU scheduling, and observability features to achieve fast scaling, low cost, and high elasticity.
Since August 2021, DeWu App has built a cloud‑native, multi‑cluster Kubernetes platform that uses an OAM‑style CloneSet model, Helm‑generated resources, Karmada‑based federation, custom scheduler plugins for reservation and node‑balance, offline mixing for Flink, a unified KubeAutoScaler, and a self‑built KubeAI stack, achieving significant cost cuts and improved stability while planning further middleware containerization and multi‑cloud expansion.
This article explains why and how to use Kubernetes features such as multiple pod replicas, PodDisruptionBudgets, deployment strategies, health probes, graceful termination, anti‑affinity, resource limits, and autoscaling to build zero‑downtime, highly available applications.
KapacityStack is an open‑source, cloud‑native capacity platform from Ant Group that introduces the Intelligent Horizontal Pod Autoscaler (IHPA) to provide predictive, multi‑level, and stable autoscaling, reducing resource waste, carbon emissions, and operational costs while supporting extensible, modular integration with Kubernetes workloads.
This article demonstrates how to achieve zero‑downtime releases for SpringBoot applications on Kubernetes by configuring readiness/liveness probes, rolling update strategies, graceful shutdown hooks, horizontal pod autoscaling, Prometheus monitoring, and externalized configuration via ConfigMaps.
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 how machine learning can be applied to automatically optimize CPU and memory settings in Kubernetes clusters, covering both experiment‑driven and observation‑driven approaches, step‑by‑step procedures, best‑practice recommendations, and the benefits of combining both methods for efficient, scalable cloud‑native operations.
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.
DeepScaling is a deep‑learning‑driven autoscaling framework that predicts workload, estimates CPU usage, and makes reinforcement‑learning‑based scaling decisions to keep microservice CPU utilization at a target level, thereby reducing resource waste while meeting SLOs in large‑scale cloud environments.
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 outlines Kubernetes' main advantages such as container orchestration, lightweight design, open‑source nature, elastic scaling and load balancing, describes typical deployment scenarios, highlights its portability, extensibility and automation, lists current drawbacks, and explains fundamental components like master, node, pod, labels, controllers, services, volumes, and namespaces.
This article details how Xingdou Cloud leverages cloud‑native techniques such as massive node overselling, custom HPA (SophonHPA), priority‑based QoS, intelligent cleanup, and quota management to achieve dramatic cost reduction and efficiency gains across its multi‑cloud platform.
This article presents a cloud-native, end‑to‑end autoscaling solution that integrates traffic forecasting, CPU utilization meta‑prediction, and a reinforcement‑learning‑based scaling decision module into a fully differentiable system, achieving higher resource utilization and cost efficiency as demonstrated by ACM SIGKDD 2022 research.
This talk presents Xiaomi's design and deployment of an elastic scheduling system for Hadoop YARN, covering background analysis, resource‑pool strategy, auto‑scaling architecture, stability challenges, label‑based resource isolation, Spark shuffle handling, cost‑saving results and future plans.
This article explains how Kubernetes Horizontal Pod Autoscaler (HPA) balances resource demand and workload by automatically scaling pod replicas, describes the different metric types it supports, walks through the core controller code (Run, worker, reconcile, and replica calculation), highlights current limitations, and shares practical observations from real‑world usage.
This article details Ctrip’s large‑scale use of AWS Spot instances on Kubernetes, explaining the cost benefits, the challenges of spot interruptions, and the architectural and operational strategies—including multi‑AZ deployment, scheduling policies, autoscaling group design, and observability—that enable a 50% reduction in container costs while maintaining system stability and reliability.