This guide outlines ten practical steps—adding load balancers, horizontal scaling, stateless services, connection pooling, aggressive caching, read replicas, task queues, auto‑scaling, WebSocket gateways, and comprehensive monitoring—to reliably handle sudden traffic spikes and keep your application responsive and cost‑effective.
This article details how GoTerra migrated from BigQuery to MaxCompute and used Resource Advisor, tiered quota strategies, and the TopN Fair scheduler to dynamically balance performance and cost across dozens of accounts and hundreds of quota groups, achieving up to 60% cost reduction.
Explore the 15 most impactful Kubernetes scenarios—from microservices and auto‑scaling to multi‑cloud deployments, AI workloads, edge computing, and compliance—detailing how they boost reliability, efficiency, and cost‑effectiveness, while also highlighting situations where Kubernetes may not be the right choice.
Explore the Go Actor model’s core concepts, compare popular Actor libraries, and follow a step‑by‑step implementation that introduces a mailbox, supervisor restart strategy, dynamic ActorPool with auto‑scaler, graceful shutdown via context, and Prometheus metrics, culminating in a complete, production‑ready concurrent framework.
This article details Qunar's end‑to‑end solution for forecasting traffic spikes, estimating required CPU resources, and automatically scaling hotel services using a combined flow‑calendar, algorithmic prediction, and Ops‑driven auto‑scaling pipeline, improving stability and operational efficiency.
Facing tight deadlines, Yahaha Studios rebuilt the STRIDEN FPS game's server deployment from a traditional Auto Scaling Group to a cloud‑native architecture using OpenKruiseGame, achieving second‑level startup, automated global scaling, lossless scaling, and significant cost reductions while improving player experience.
This article explains how Baidu's vertical search platform tackled massive data growth and scaling challenges by redesigning its data management system, introducing elastic scheduling, decoupling ETCD access, implementing auto‑scaling, and advancing shard expansion to improve performance, stability, and cost efficiency.
This document describes a comprehensive capacity‑planning solution that predicts traffic‑peak impacts for hotel services, automatically estimates required CPU resources, creates timed scaling tasks, and evaluates performance using detailed metrics, thereby improving operational efficiency and reducing manual effort during events such as exam‑ticket printing and holiday travel surges.
This article presents a hybrid‑cloud solution that uses ACK Edge and KServe to dynamically allocate on‑premise and cloud GPU resources for large‑language‑model inference, addressing tidal traffic patterns, reducing costs, and ensuring high availability through elastic scaling and custom scheduling policies.
The article explains how elastic architecture, through auto‑scaling and failover mechanisms, dynamically adjusts resources and ensures continuous service during traffic spikes and component failures, improving cost efficiency, reliability, and operational stability for modern cloud‑based applications.
After fully containerizing their platform, the team tackled front‑line development scaling challenges by designing a custom elastic‑scaling solution that combines dual‑threshold and timed scaling, integrates hybrid‑cloud ClusterAutoScale, consolidates middleware resources, and implements a comprehensive K8s observability stack, delivering over 30% additional compute capacity and near‑perfect scaling reliability.
This article explains how to configure Kubernetes Horizontal Pod Autoscaler (HPA) for automatic pod scaling, covering core concepts, metric selection, and two detailed YAML examples that demonstrate scaling based on CPU utilization and custom data‑processing rates.
The article explains how to use throttling together with auto‑scaling to control resource consumption of cloud applications, prevent tenant overload, handle traffic bursts, and ensure service‑level agreements while optimizing costs.
To overcome native HPA limits and business‑specific constraints in a fully containerized, cloud‑native Kubernetes environment, we implemented a dual‑threshold water‑level and scheduled scaling engine, hybrid‑cloud ClusterAutoScale, mixed‑deployment resource prioritization, and comprehensive Prometheus‑based observability, achieving higher utilization, lower costs, and a roadmap toward deeper optimization and AIOps.
The Taobao Open Platform adopts a cloud‑native, multi‑tenant architecture that abstracts infrastructure, isolates tenants via independent or shared switch‑plus‑security‑group schemes with dual ENI pod networking, and leverages Kubernetes auto‑scaling to simplify onboarding, cut operational costs, and enable future low‑code and FaaS extensions.
ByConity is a cloud‑native, storage‑compute separated data warehouse engine that supports multi‑tenant isolation, high performance, and elastic scaling; this guide explains its three‑layer architecture, hardware requirements, Helm‑based Kubernetes deployment, dynamic scaling, and practical SQL testing steps.
This article details Hupu's massive sports‑traffic environment, the design and implementation of the Flex auto‑scaling platform, its architecture, core functions such as resource statistics, node and pod scaling, scenario scheduling, and the performance optimizations that enable rapid, cost‑effective scaling across multi‑cloud Kubernetes clusters.
This guide walks through designing and deploying a highly available, scalable WordPress web application on JD Cloud using elastic IaaS, PAAS services, load balancers, HA groups, Docker containers, MySQL, Redis, and automated scripts to demonstrate fault‑tolerance across availability zones.
The article presents high_service, a high‑performance inference framework that boosts GPU utilization in multimedia AI services by separating CPU‑heavy preprocessing from GPU inference, employing priority‑based auto‑scaling, multi‑tenant sharing, and TensorRT‑accelerated models to eliminate GIL bottlenecks, reduce waste, and adapt to fluctuating traffic, with future work targeting automated bottleneck detection and further CPU‑GPU offloading.
The session detailed how Tencent’s container experts boost Kubernetes cluster utilization by correcting pod resource requests, employing two‑level auto‑scaling, dynamic over‑commit, adaptive scheduling and eviction, and using HPA/EHPA/VPA, achieving up to 38.7% node usage and roughly 60% cost savings in real‑world workloads.
Knative is a CNCF‑incubated, cloud‑native serverless framework on Kubernetes that combines Build, Eventing, and Serving components—featuring a Knative Pod Autoscaler that can scale pods to zero—offering improved resource utilization, rapid traffic response, and developer productivity despite modest performance overhead.
This article explains the concept of elastic auto scaling in cloud services, describes typical scenarios such as high‑elastic web apps and compute‑intensive workloads, details the four‑layer architecture and workflow, and outlines functional features, stability improvements, and future optimization directions.
Shilong Fei from Xiaomi Data Platform presents an in‑depth exploration of elastic scheduling for Hadoop YARN, covering background, design of resource pools, auto‑scaling architecture, challenges such as job stability and user transparency, achieved cost reductions, and future plans for further optimization.
This article describes the design and implementation of an automated FlinkSQL tuning system that monitors metrics, evaluates task health with rule‑based logic, calculates optimal resource adjustments, and performs fast scaling to reduce cluster waste, lower operational costs, and maintain SLA compliance.
Shopee reduces data‑center carbon emissions by over 40,000 tons annually through three 2021 green‑computing technologies—Overcommit resource oversubscription, mixed‑model Colocation of latency‑sensitive and batch workloads, and enhanced Auto Scaling that leverages global metrics to cut machine usage and improve resource efficiency.
This article presents the design and implementation of Flink task auto‑scaling, covering background, manual and automatic scaling mechanisms, architecture with RescaleCoordinator, persistence via Zookeeper and HDFS, scaling policies for parallelism, CPU and memory, and future plans for fine‑grained and time‑based resource adjustments.
The article describes Liulishuo's Cloud Infra team's end‑to‑end automation of cloud resource provisioning, scaling, and cost governance using Terraform, a custom Luban platform, GitLab CI/CD, and chat‑bot integrations, highlighting the architectural design, implementation steps, and measurable benefits for both operations and business teams.
TDSQL‑C Serverless separates compute and storage, delivers instant elastic scaling for MySQL and PostgreSQL, charges per‑second usage, pauses and stops billing when idle, and supports low‑frequency, archival, development, and micro‑service workloads with a ~2‑second cold‑start.
Facing surges during holidays and major shopping events, Dada’s DevOps team built a cloud‑native elastic scaling system that combines fine‑grained capacity management, multi‑cloud support, metric‑driven auto‑scaling, and extreme‑scale down strategies, delivering stable delivery performance while cutting costs.
This article explains how to achieve high availability for stateless services by employing redundancy, vertical and horizontal scaling, various load‑balancing algorithms (random, round‑robin, weighted, least‑connections, source‑hash), and automatic scaling techniques in cloud‑native environments, while also covering performance monitoring and CDN/OSS usage.
This article explains how to design highly available stateless services by covering redundancy, vertical and horizontal scaling, load‑balancing algorithms, high‑concurrency identification, and the use of CDN/OSS, offering practical guidance for robust backend architecture.
After migrating to the cloud, enterprises must evaluate resource scale, cost pressure, and staffing before deciding whether to build their own scheduling system, and can choose among ECS, Dedicated Host, or private pool solutions, each with specific advantages, drawbacks, and suitable scenarios.
This article explains how consistent hashing, combined with Docker-based microservice architectures, reduces data movement during dynamic scaling by using a ring‑shaped hash space and intelligent node placement, making auto‑scaling more efficient and resilient.
This article details Dada Group’s implementation of an intelligent elastic scaling architecture that automatically adjusts capacity during peak promotions and low‑traffic periods, improving delivery reliability, reducing costs, and supporting multi‑cloud and multi‑runtime environments through sophisticated monitoring and auto‑scaler mechanisms.
This article presents Dada Group's comprehensive approach to elastic capacity management and automated scaling, detailing the challenges faced during traffic spikes, the design of a cloud‑native auto‑scaler, multi‑metric observability, decision‑making logic, execution mechanisms, extreme scaling practices, and future optimization directions.
Elastic scaling, a cornerstone of Xianyu’s shift to serverless cloud-native architecture, leverages Kubernetes autoscaling components—Cluster‑Autoscaler, HPA, VPA—to dynamically adjust resources via reactive thresholds or predictive models, yet faces challenges like cold‑starts, lack of scale‑to‑zero, and optimal pod‑pool buffering, prompting ongoing research for faster, smarter, safer scaling.
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 article explains what containers are, introduces Kubernetes as a leading open‑source container orchestration platform, and details its key advantages such as universal deployment, seamless cloud migration, efficient resource use, built‑in auto‑scaling, simplified CI/CD, and high reliability.
This article explains how Alibaba's New‑Retail Business Framework (NBF) implements a non‑typical FaaS architecture that delivers full Serverless capabilities—including containerized bundle management, service publishing, routing, fault tolerance, millisecond‑level auto‑scaling, and rapid rollback—proving its reliability during large‑scale promotional events.
Tencent’s billing platform powers billions of daily transactions across 180+ countries, supporting both consumer and business payments, and employs sophisticated capacity testing, dynamic auto‑scaling, resource sharing, and change‑control mechanisms to ensure reliable large‑scale promotional events without service disruptions.
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.
The guide outlines a scalable, globally unified architecture for WeChat mini-games—combining stateless compute layers with CLB, auto-scaling, a star-router model for peer communication, and horizontally-sharded storage using Tencent Cloud’s DCDB or TCaplus to handle rapid viral growth.
This article explains how 1号店 built a distributed search engine with horizontal scaling, custom sharding and routing strategies, automated deployment, rapid expansion, and real‑time monitoring to handle the massive traffic spikes of the annual 11.11 e‑commerce promotion.
This article explains why Cainiao needed an elastic scheduling system, how its unique business and technical characteristics make it ideal for such a solution, and details the architecture, decision‑making layers, strategies, and real‑world results that together improve resource utilization, stability, and cost efficiency.
The article details Alibaba's Taobao Personalization Platform (TPP) intelligent scheduling system, explaining its architecture, optimization algorithms, convergence logic, and performance results that dramatically improve CPU utilization and automate scaling during both regular operation and high‑traffic events like Double‑11.
This article explains how to build a DevOps workflow using container orchestration, covering agile development with Docker image slimming, a Jenkins‑based CI/CD pipeline, simplified Gitflow, container monitoring with cAdvisor‑InfluxDB‑Grafana, and auto‑scaling via Marathon, while separating automated deployment from production release.
This article explains the differences between monolithic and microservice architectures, introduces Docker as a key tool for microservices, and details how a Container‑as‑a‑Service (CaaS) platform can provide service discovery, routing, logging, monitoring, auto‑scaling, SLA enforcement, and deployment strategies to simplify development and operations.
This article explains how to design and operate feature toggles in Java applications, covering single‑instance implementation, cross‑instance synchronization via a meta‑server or Diamond, handling composite switches, avoiding security pitfalls, and automating degradation and upgrade based on runtime metrics.
Qiniu Cloud transformed its high‑traffic data processing platform by containerizing services with Docker, addressing challenges such as massive request volume, CPU‑intensive workloads, IO bottlenecks, and burst traffic through architecture evolution, queueing, rate limiting, auto‑scaling, and secure, isolated custom processing pipelines.
This article explains how a major e‑commerce platform built a horizontally scalable distributed search engine, designed efficient sharding and routing strategies, and implemented automated deployment, rapid scaling, and real‑time monitoring to handle the massive traffic of the 11.11 shopping festival.
The article shares the author’s experience designing and operating enterprise‑grade cloud platforms, covering resource and application management, the Platform EGO architecture, comparisons with Mesos, Yarn and Kubernetes, and practical insights on scaling, scheduling, security, and architectural evolution.
This article explains how TDSQL transforms MySQL into a distributed, high‑availability SQL system by addressing NoSQL limitations, introducing a Scheduler‑Agent‑Gateway architecture, automatic scaling, sharding, robust disaster‑recovery mechanisms, and future integration with container technologies.
