This article explains why scaling GPU inference services on Kubernetes is challenging and presents a multi‑layer control architecture, metric upgrades, and production‑ready implementations using HPA, KEDA, KServe, and Karpenter to achieve stable, cost‑effective autoscaling.
This guide walks through building a production‑grade Kubernetes GPU cluster for large language model inference, covering hardware sizing, GPU resource scheduling, model storage options, automated scaling with HPA, health checks, monitoring, troubleshooting, and multi‑model deployment strategies.
This article explains how the cloud‑native Knative serverless framework reduces GPU waste, enables request‑driven autoscaling to zero, accelerates AI model versioning and startup with Fluid, and integrates protocols like MCP and A2A to deliver cost‑effective, high‑performance AI inference services.
By rethinking resource requests, eliminating unused workloads, downsizing node types, fine‑tuning autoscaling, and trimming log storage, a team reduced their Kubernetes bill by 40% while keeping the same cloud provider, demonstrating that most cost overruns stem from misconfiguration rather than the platform itself.
The article details Baidu Baige’s next‑generation distributed inference platform for trillion‑parameter LLMs, explaining how automated orchestration, the FedDeployment abstraction, SplitService unified view, Adaptive HPA predictive scaling, Silent Instances for second‑level activation, and the Staggered Batched Scheduler eliminate scaling limits, reduce TTFT by 30‑40%, boost throughput by up to 20%, and achieve cost‑effective, elastic AI compute.
Horizontal Pod Autoscaler (HPA) in Kubernetes automatically scales pod replicas based on metrics like CPU, memory, or custom indicators, and this guide explains its core principles, configuration pitfalls, step‑by‑step troubleshooting commands, and advanced considerations such as API versions, stabilization windows, and integration with Cluster Autoscaler.
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.
This article explores the challenges and best practices of cloud‑native scaling, covering Horizontal and Vertical Pod Autoscalers, Cluster Autoscaler cost optimization, event‑driven scaling with KEDA, traffic‑aware scaling in service meshes, and intelligent cost‑aware strategies backed by monitoring and future AI‑driven trends.
Facing a 35‑million‑yuan monthly Kubernetes bill, the author analyzed hidden cost components, implemented five optimization campaigns—including resource request tuning, autoscaling, spot instances, storage tiering, and network consolidation—and reduced monthly expenses by 60% while boosting performance, delivering a detailed, reproducible methodology.
This article explains how to design elastic scaling architectures on cloud platforms—combining horizontal, vertical, and functional scaling, leveraging Kubernetes autoscaling features, predictive scaling, mixed instance strategies, and cost‑monitoring practices—to handle traffic spikes while minimizing expenses.
This article explains how Karpenter's NodePool CRD replaces traditional provisioners, details its core configuration fields, illustrates the autoscaling workflow from a pending pod to a ready node, and shows how to achieve cost‑effective, on‑demand resource provisioning in Kubernetes clusters.
This comprehensive guide walks you through containerizing applications, optimizing Docker images, securing containers, designing Kubernetes high‑availability clusters, implementing observability with Prometheus and ELK, automating CI/CD pipelines, applying RBAC and network policies, and cutting costs with autoscaling and resource tuning, all backed by real‑world code examples.
An analysis of recent Kubernetes cost benchmarks reveals chronic over‑provisioning, with up to 40% idle CPU and 57% idle memory, and offers six community‑validated, actionable automation techniques—including flexible instance selection, arm migration, custom autoscaling, bin‑packing, VPA, and safe Spot usage—to dramatically reduce cloud spend.
This comprehensive guide walks you through Kubernetes fundamentals, cluster architecture, key components, resource objects, installation steps, and advanced features such as Ingress, RBAC, CronJobs, and Horizontal Pod Autoscaling, providing practical commands and examples for real‑world deployments.
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 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 walks you through deploying NVIDIA NIM for LLM inference on Alibaba Cloud ACK, integrating the Cloud Native AI Suite, configuring KServe, setting up Prometheus and Grafana monitoring, and implementing custom autoscaling based on request queue metrics.
This article outlines the most frequent Kubernetes pitfalls—such as missing resource requests, omitted health checks, using the :latest tag, over‑privileged containers, insufficient monitoring, default namespace misuse, weak security settings, absent PodDisruptionBudgets, lack of pod anti‑affinity, and improper load‑balancing—and provides concrete commands, YAML examples, and best‑practice recommendations to prevent them.
Facing exploding Kafka scale, Xiaohongshu’s data‑storage team adopted a cloud‑native design that introduces tiered hot‑cold storage, containerization, and a custom load‑balancing service, achieving dramatic storage‑cost reductions, minute‑level cluster migrations, high‑performance data access, and automated resource scheduling.
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 how Knative leverages request‑driven traffic management to simplify blue‑green releases, configure multi‑gateway ingress, apply revision garbage‑collection policies, enable custom domains, support multiple protocols, and provide automatic scaling and observability through Prometheus and Grafana.
This article explains how Knative implements request‑based autoscaling with KPA, details the scale‑to‑zero mechanism, shows how to handle burst traffic using stable and panic windows, and demonstrates advanced extensions such as resource pools, precise MPA scaling, and predictive AHPA configurations with concrete YAML examples.
Learn how to configure Kubernetes for zero‑downtime applications by syncing container images, ensuring multiple pod replicas, using PodDisruptionBudgets, selecting appropriate deployment strategies, setting up liveness/readiness probes, handling graceful termination, applying pod anti‑affinity, and enabling autoscaling and proper resource limits.
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.
The MagicScaler framework, introduced by Alibaba Cloud’s big‑data engineering team and collaborators, combines a multi‑scale attention Gaussian process predictor with an uncertainty‑aware elastic scaling decision engine, delivering significantly higher quality‑of‑service and lower operational costs than traditional autoscaling methods, as demonstrated on real MaxCompute workloads.
A VLDB 2023 Industrial Track paper by Alibaba Cloud introduces MagicScaler, a predictive autoscaling framework that combines multi‑scale attention Gaussian processes with uncertainty‑aware optimization to deliver high QoS at reduced cost for cloud resources.
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 presents practical Kubernetes best‑practice cases—including horizontal autoscaling, service discovery with load‑balancing, and health‑check probes—to help engineers build more reliable, scalable, and self‑healing container deployments.
This guide explains how to combine Kubernetes and Spring Boot to implement zero‑downtime releases by configuring readiness and liveness probes, defining graceful shutdown, applying rolling update strategies, setting up horizontal pod autoscaling, integrating Prometheus monitoring, and separating configuration via ConfigMaps for reusable images.
This article explains how Kubernetes CPU requests and limits work, why limits can throttle performance, compares language‑specific behaviors, and presents alternative strategies such as relying on requests with Horizontal Pod Autoscaling for more efficient and cost‑effective scaling.
This article explains key Kubernetes concepts—including the kube-proxy IPVS mode versus iptables, static Pods, Pod lifecycle states, Pod creation workflow, restart policies, health probes, scheduling strategies, init containers, Deployment upgrade processes and strategies, DaemonSet characteristics, Horizontal Pod Autoscaling, Service types and load‑balancing, headless Services, external access methods, Ingress routing, image pull policies, and load‑balancer options—providing a comprehensive overview for cloud‑native practitioners.
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.
This article walks through the complete lifecycle of a Kubernetes deployment, explaining how a manual upgrade request triggers API calls, creates Deployments, ReplicaSets, Pods, and how the scheduler, kubelet, and Docker work together, while also covering concepts like containers, labels, replication controllers, deployments, and autoscaling mechanisms.
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 presentation details Huolala's end‑to‑end cloud‑native cost‑optimization strategy, covering the company's infrastructure basics, Kubernetes‑based server cost‑saving techniques, a tailored optimization roadmap, practical Spot Instance usage, and a custom CronHPA‑driven scheduled scaling solution to boost resource utilization.
This article introduces the Advanced Horizontal Pod Autoscaler (AHPA), explains its three‑stage architecture of data collection, prediction, and scaling, details the RobustScaler forecasting algorithm and CRD‑based deployment, and evaluates its ability to proactively and reactively adjust pod counts with high robustness.
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 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.
Huolala’s architecture team shares a detailed walkthrough of their cloud‑native cost‑optimization journey, covering public‑cloud server pricing models, Kubernetes request/limit tuning, HPA and CronHPA scheduling, spot instance integration, intelligent pod placement, and practical lessons learned from scaling a global on‑demand logistics platform.
This article details AutoStream's evolution from a Java‑only Storm platform to a Flink‑based, Kubernetes‑native streaming system that integrates budgeting controls, automatic scaling, lakehouse architecture with Iceberg, and PyFlink support, highlighting the technical challenges, solutions, and future roadmap for real‑time analytics.
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.
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 shares a practical guide to the most frequent Kubernetes pitfalls—from misconfigured resource requests and limits to improper liveness/readiness probes, load‑balancer settings, IAM misuse, pod anti‑affinity, and disruption budgets—offering concrete YAML examples and remediation steps to help operators run more reliable and cost‑effective clusters.
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.
This article explains how Tencent Cloud EMR integrated Hadoop YARN with Kubernetes Pods to create a hybrid online‑offline deployment, implement elastic autoscaling and multi‑label resource allocation, and achieve several‑hundred‑percent improvements in CPU utilization while preserving cluster stability.
This article explains how to scale Kubernetes clusters and applications using tools like Cluster Autoscaler, Virtual Kubelet, HPA, VPA, and especially KEDA, while showcasing Alibaba Cloud EDAS's integration and future AI‑driven autoscaling enhancements.
This article explores Knative’s traffic routing and autoscaling mechanisms, detailing how requests are initially directed through the Activator during cold‑start, how VirtualService configurations evolve, and how newer versions shift traffic handling to Kubernetes Service/Endpoint layers, improving performance and decoupling gateway logic.
This article explains Knative’s traffic‑driven autoscaling and gray‑release capabilities, detailing the request flow architecture, the roles of Service, Configuration, Route and Revision, and walks through built‑in scaling strategies such as KPA, HPA, scheduled‑HPA, event‑gateway and custom plugins, with practical examples.
This article describes how a tech team built a catalog‑based cost‑allocation system, leveraged cloud tags and Kubernetes labels, used Prometheus data for scaling decisions, and combined reserved, spot, and on‑demand instances to boost cloud resource utilization while keeping services stable.
This article outlines comprehensive production‑grade Kubernetes best practices—including health probes, RBAC, resource management, network policies, monitoring, autoscaling, image security, and zero‑downtime strategies—to help teams run secure, efficient, and highly available workloads.
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 article outlines frequent Kubernetes operational mistakes—such as misconfigured resource requests, missing probes, improper load‑balancer exposure, naïve autoscaling, IAM/RBAC misuse, lack of anti‑affinity, absent PodDisruptionBudgets, multi‑tenant pitfalls, and suboptimal externalTrafficPolicy—providing concrete remediation steps and best‑practice code examples.
This article explains how Xiaomi migrated tens of thousands of Redis instances from bare‑metal servers to Kubernetes, using Redis Proxy, StatefulSets, and Ceph storage to achieve resource isolation, automated deployment, dynamic scaling, and improved reliability while addressing latency, IP‑change, and security challenges.
This article details how Tubi built the Tubi Data Runtime platform on JupyterHub using Kubernetes, covering authentication with Okta SSO, custom Docker images, shared EFS storage, multi‑service support, GPU enablement, node affinity, cluster autoscaling, and monitoring with Prometheus.
This article introduces the fundamentals of serverless architecture, explains how Docker containers and Kubernetes orchestration enable dynamic scaling, and outlines various autoscaling mechanisms and scenarios for elastic resource management in modern cloud-native applications.
The article explains Kubernetes’ static scheduler causes fragmented, under‑utilized clusters, then proposes dynamic techniques—pod resource compression, node resource oversell via admission webhooks, and an enhanced per‑HPA autoscaling controller—while outlining future scheduler extensions, monitoring integration with Tencent Cloud, and a senior cloud‑native engineer recruitment call.
This article explains how serverless and autoscaling complement Kubernetes by detailing resource‑capacity curves, stakeholder needs, core autoscaling components, key challenges, design philosophy, classic use cases, limitations of traditional scaling, and the emerging virtual‑kubelet‑autoscaler solution.
The article details NetEase's experience designing and implementing dynamic server scaling for overseas games on AWS, comparing Auto Scaling and GameLift, describing a custom scaling platform, the challenges faced, lessons learned, and future directions for cloud‑based game operations.
Alibaba Cloud’s Kubernetes service integrates four native monitoring services—SLS, ARMS, AHAS, and Cloud Monitor—while offering enhanced open‑source components and autoscaling mechanisms such as HPA, VPA, cronHPA, Resizer, Cluster‑Autoscaler, and virtual‑kubelet‑autoscaler, enabling cloud‑native apps to achieve robust observability and elastic scaling.
Meitu’s container platform, built on Kubernetes with Calico networking, a custom Nginx load‑balancer, unified logging, refined scheduling, autoscaling, and comprehensive monitoring, enables seamless multi‑cluster hybrid‑cloud operations for its hundreds‑of‑millions‑user services while providing CI/CD tooling and future‑ready extensions such as service mesh and edge computing.
This article explains why elastic scaling is needed for variable web traffic, describes how to build a cost‑effective, automatically adjustable service on Alibaba ECS using message queues, service refactoring, Docker deployment, logging, and a real‑time allocation algorithm, and shares practical lessons learned.
This article shares the engineering team’s experience of building a high‑growth, reliable backend for English Fluently, covering inter‑service communication with gRPC, service discovery, Docker‑based deployment, health‑checking, monitoring, autoscaling, Kubernetes orchestration, and multi‑cell availability strategies.
