Tencent Games’ data governance team details an AI‑driven, end‑to‑end semantic framework that shifts traditional rule‑based data management to a service‑oriented model, cutting storage waste by 30 %, halving development time, and boosting asset recommendation accuracy to 95 % across its global gaming platform.
The article details how Huolala’s Dolphin platform engineers large‑model inference for high‑concurrency, long‑context, low‑latency production workloads, achieving 50‑60% GPU cost reduction through systematic resource allocation, model quantization, PD‑separation, speculative sampling, and kernel‑level optimizations while maintaining service stability.
This article examines the shortcomings of traditional big‑data engines for AI workloads, presents a Ray‑based heterogeneous fusion architecture that unifies CPU/GPU scheduling, Python ecosystems, and streaming‑batch processing, and details fault‑tolerance, checkpointing, compute‑storage separation, resource‑utilization, scalability, and observability improvements that enable thousands of nodes and dramatically higher GPU efficiency.
This article summarizes Quincy Qu's COLING 2025 industry‑track paper that proposes a three‑stage multi‑task LLM framework for large‑scale NLP services, achieving performance comparable to single‑task models while reducing overall service cost by up to 90.9%.
JD Retail outlines the growing data management challenges it faces—including asset discovery, architecture agility, development quality, and rising IT costs—and presents a comprehensive data governance framework that leverages standards, agile architecture, development isolation, and resource optimization to improve efficiency and reduce operational expenses.
JD Retail faces growing data volume, redundant models, and resource‑intensive storage, prompting a comprehensive data‑governance strategy that defines standards, streamlines architecture, isolates development, and optimizes compute and storage costs, ultimately enabling more efficient, secure, and agile data operations across the enterprise.
JD Retail confronts growing data volume, redundant models, shared account risks, and rising storage costs, and responds with a comprehensive data governance framework that standardizes data, streamlines architecture, isolates development, and optimizes resources to achieve efficient, secure, and cost‑effective data operations.
This paper introduces ComRecycle, an intelligent computation recycling framework for Taobao's display advertising system that caches and reuses ad candidates across recall, coarse‑ranking, and fine‑ranking stages, achieving up to 23% CPU and 22% GPU savings while maintaining recommendation quality.
A former monolith‑to‑containers migration revealed hidden performance penalties—namespace conversion, network overhead, storage I/O, and resource contention—plus over‑decomposed microservices, memory overallocation, bloated images, and mis‑tuned orchestration, all of which can be diagnosed and remedied with systematic measurement, tracing, and configuration adjustments.
OPPO’s big‑data platform adopts a hybrid‑cloud architecture that combines on‑premise IDC resources with public‑cloud elasticity, addressing massive data volumes, cost, security, and vendor lock‑in while delivering higher resource utilization, stability, and autonomous evolution for future workloads.
Facing the challenges of a billion‑user membership platform, we analyzed front‑end performance metrics, applied resource slimming, lazy loading, network optimizations, and SSR/pre‑rendering techniques, achieving up to 73% LCP reduction and dramatically improving page load speed and user retention.
The article presents MaRCA, a multi‑agent reinforcement learning framework that models user value, compute consumption, and action reward to allocate limited computation resources across the entire advertising recommendation pipeline, achieving higher ad revenue while keeping system load stable under fluctuating traffic and diverse request values.
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 article explains how to combine online services and offline tasks within a single Kubernetes cluster using offline‑online hybrid deployment, detailing its benefits such as cost savings and higher resource utilization, and walks through practical implementation methods like CronJobs, HPA, priority classes, node affinity, custom schedulers, and the open‑source Koordinator project, while also addressing associated challenges.
JD.com’s data‑governance framework combines a health‑score‑driven, automated platform that cross‑verifies audit logs, builds full‑link and operator‑level lineage, introduces standard fields, and optimizes resource mixing, task staggering, and cross‑datacenter scheduling, while targeting real‑time AI‑enhanced detection and full automation.
This article presents JD.com’s comprehensive big‑data governance experience, detailing the background and challenges, the automated governance platform and its core technologies such as audit logs and full‑link lineage, strategies for resource optimization, and the roadmap toward real‑time, intelligent, and fully automated data governance.
This article explains how Kubernetes’ native scalability, resource optimization, performance tuning, portability, and fault‑tolerance features align with the demands of AI inference, helping organizations run large ML models efficiently, cost‑effectively, and reliably across diverse environments.
This guide explains what Kubernetes VPA is, its architecture, version compatibility, step‑by‑step installation, certificate setup, manifest generation, practical VPA configuration, validation procedures, performance testing, and known limitations, enabling you to optimize pod resources in cloud‑native clusters.
Kubernetes aligns perfectly with AI inference demands by offering built‑in scalability, resource and performance optimization, seamless portability across clouds, and robust fault‑tolerance, making it a cost‑effective, high‑availability foundation for deploying large‑scale machine‑learning models.
Bilibili’s one‑stop Big Data Cluster Management Platform (BMR) consolidates HDFS, Spark, Flink, ClickHouse, Kafka and other services into a unified system that evolved through four stages—standardization, metadata‑driven construction, containerization, and observability—addressing node consistency, scaling, fault self‑healing, and resource optimization while delivering elastic scaling, automated start/stop, and future cost‑saving and stability enhancements.
This article details Didi's comprehensive big data cost governance framework, covering its data architecture, asset management scoring, Hadoop and Elasticsearch cost optimization methods, and practical insights on organizational processes and incentives for effective cost control.
This article details DeWu's cloud‑native transformation since August 2021, covering multi‑cluster federation, application profiling, custom scheduling plugins, resource pre‑reservation, co‑location of online and offline workloads, cost‑saving hardware choices, multi‑cloud strategy, and the development of the KubeAI platform for AI scenarios.
Vivo’s online‑offline co‑location platform consolidates latency‑sensitive online services and batch offline workloads on shared Kubernetes nodes, using differentiated resource views, priority‑based QoS, and safety watermarks to boost CPU utilization from 13 % to 25 %, adding 20 000 cores and 50 TB memory for peak‑hour offline tasks.
ByteDance’s DataLeap suite tackles the complexities of large‑scale data platform governance by identifying manual tuning pain points, deploying automated rule‑engine recommendations, and optimizing resource allocation, ultimately improving stability, reducing costs, and enhancing overall system health across thousands of Spark, Flink and other tasks.
This article details Bilibili's FinOps-driven approach to IT cost management, covering budgeting, resource utilization dashboards, financial metrics, cost modeling, SKU pricing, and operational practices that together saved the company hundreds of millions of yuan while supporting continued growth.
This article details how the front‑end team tackled the massive traffic and performance challenges of the Spring Festival Gala interactive H5 by optimizing static resources, employing WebP and sprite techniques, implementing traffic‑shaping and localStorage caching, and automating engineering workflows to ensure a stable, smooth user experience.
This article explains how the Android resources.arsc file is generated, identifies the problem of duplicate and uncompressed image resources in multi‑module projects, and presents a Gradle‑based solution that inserts a custom task after processReleaseResources to deduplicate resources, compress images using pngquant, guetzli and cwebp, and repack the .ap_ files, resulting in significant APK size reduction.
This article examines the challenges of uneven compute resource distribution across China and presents Yum China's practical approaches—including multi‑unit deployment, dual‑data‑center scheduling, and supporting platforms—to achieve flexible, cost‑effective compute scheduling for the restaurant sector.
Katalyst v0.4.0 introduces tidal colocation for mixed workloads, online resource overcommit, fine‑grained NUMA memory management, OOM priority enhancements, and topology‑aware scheduling, providing a comprehensive cost‑optimization solution for cloud‑native Kubernetes clusters.
This article details ByteDance's end‑to‑end event‑tracking cost governance, covering background, strategies, large‑scale data pipelines, resource challenges, control mechanisms, automated and supervised governance modes, and the substantial savings achieved through point filtering, tiered prioritization, and sampling.
This article describes a systematic approach to reducing mobile app package size by detecting oversized resources, scanning unused assets, enforcing size checkpoints during development, and integrating automated validation into the CI/CD pipeline, illustrated with tools, workflows, and results.
This article presents Didi's comprehensive big data cost governance approach, detailing the overall framework, data system architecture, asset management platform, Hadoop and Elasticsearch cost‑control practices, metadata‑driven optimization, and organizational insights for effective resource and budget management.
Koordinator, an open‑source cloud‑native mixed‑workload scheduler born from Alibaba’s internal container orchestration experience, enables Xiaohongshu to reclaim idle resources, improve CPU utilization beyond 45%, reduce resource costs by millions of core‑hours, and seamlessly integrate Kubernetes with YARN for batch and AI workloads.
This article examines the challenges faced by Volcano Engine's DataLeap in computational governance, outlines automated solutions such as real‑time rule engines and monitoring, and presents concrete performance and cost benefits achieved through resource optimization across large‑scale Spark and Hadoop workloads.
The forum held on October 25, co‑organized by China Financial Computer magazine and Ant Group, gathered experts from eleven financial institutions to share cloud‑native and green‑computing practices—including distributed stacks, hybrid deployments, secure containers, serverless, and continuous profiling—aimed at advancing low‑carbon, high‑quality compute in the financial sector.
This article explains the challenges of memory management in mixed workloads, outlines the limitations of native Linux and Kubernetes mechanisms, and details how ByteDance’s open‑source Katalyst Memory Advisor improves memory utilization, QoS, and eviction handling through user‑space policies, multi‑dimensional interference detection, and adaptive mitigation actions.
This article explains the challenges of memory management in mixed Kubernetes workloads, introduces ByteDance's open‑source Katalyst Memory Advisor, details native allocation and reclamation mechanisms, outlines its architecture and plugins, and describes interference detection and multi‑level mitigation strategies to improve memory utilization and service quality.
The article explains how capacity management balances cost control and service quality through defined goals, three development stages, detailed resource optimization methods, stress‑testing metrics and standards, and automated scaling to achieve significant cost reductions while maintaining system stability.
This article analyzes the problem of test resource scarcity in software development teams and proposes an elastic R&D approach that dynamically balances development and testing workloads to maximize delivery efficiency without changing team composition.
DeWu reduces its APK size by removing unused plugin resources, compressing images with WebP, Guetzli, PNGQuant and Gifsicle, deduplicating and obfuscating ARSC entries, applying 7‑zip compression, delivering large assets dynamically, and continuously cleaning unused resources through compile‑time checks and runtime scanning.
This article explains how to analyze and automatically optimize complex DAG‑based data platform task chains, identify bottlenecks, adjust upstream task timings, and reduce critical‑path execution time by up to 30 minutes while preventing resource contention and peak overloads.
This article explains why tag scoring is essential for data governance, outlines a five‑dimensional scoring model—including usage, attention, quality, continuous optimization, and security—and demonstrates how the scores can drive dashboards, alerts, and resource‑saving decisions.
Baidu MEG’s offline big‑data platform cut costs and boost efficiency by applying intelligent scheduling, storage‑separation, tide‑power workload profiling, remote shuffle services and dynamic quota resizing, raising compute utilization from 55 % to 80 % and storage from 63 % to 78 %, slashing annual expenses by roughly ¥70 million and reducing task duration by about 30 %.
The article explains how Baidu’s iOS app reduces package size by examining Mach‑O binaries with tools like otool and MachOView, then applying Python scripts to locate oversized assets, eliminate unused configuration files, and deduplicate resources, achieving a 12 MB reduction and a repeatable optimization pipeline.
This article explains how Baidu's iOS app analyzes Mach‑O binaries and applies systematic resource‑optimization techniques—including big‑resource detection, unused config removal, and duplicate asset elimination—to shrink package size by over a dozen megabytes.
This article introduces Amiya, a self‑developed overcommit component that dynamically increases Yarn memory and vCore capacity on Bilibili's offline big‑data clusters, details its architecture, key implementation of overcommit, eviction and mixed‑deployment strategies, and evaluates its resource‑utilization impact.
To replace wasteful physical‑machine scripts on Baidu’s Aladdin search platform, the team adopted OpenFaaS on a Kubernetes cluster, using its event‑connector, Prometheus‑driven autoscaling, and high‑availability gateway to run synchronous, scheduled, and asynchronous functions, achieving stable deployments with only 14 % CPU and 8 % memory of the original.
This article details NetEase Media's migration of most Flink jobs to a self‑built real‑time platform on Kubernetes, covering the benefits of K8s isolation, the chosen native deployment mode, performance‑critical optimizations, monitoring, resource‑recommendation, and future directions for cloud‑native streaming workloads.
This article details Bilibili's journey from rapid data growth to a structured big‑data governance framework, describing the challenges of fragmented ownership, the "Wanglou" project launch, asset metadata modeling, metric design, user engagement strategies, automation tools, and the shift from reactive to proactive, multi‑dimensional resource management.
Sysdig’s 2023 Cloud‑Native Security and Usage Report uncovers that most container images carry critical vulnerabilities, a majority of granted permissions go unused, many containers lack proper CPU limits, and significant cloud‑cost waste persists, prompting urgent recommendations for identity‑access management, vulnerability prioritisation, and zero‑trust adoption.
Facing rapid growth of its Android app size beyond 117 MB, JD Finance implemented a comprehensive APK slimming project in 2022, analyzing APK components, SDKs, and ZIP structure, then applying image optimization, R‑file inlining, resource obfuscation, dynamic SO loading, and build‑time configurations to reduce the package to 74 MB and improve download conversion rates.
Bilibili's capacity management platform integrates foundational data, VPA/HPA scaling, quota control, and visual dashboards to streamline resource usage, cut costs, and boost stability, delivering event‑specific support such as for S12 that slashes release issues by 80% and online failures by 90%, while planning predictive scaling and risk control.
NetEase Cloud Music transitioned from public FaaS to a private Knative‑based serverless platform, delivering elastic audio‑video processing, multi‑language support, event‑driven scaling across hybrid private and public clouds, boosting resource utilization and cutting costs, though cold‑starts and container expertise remain challenges.
Koordinator, an open‑source cloud‑native scheduler from Alibaba, enhances container performance and reduces cluster costs by introducing mixed‑workload placement, resource profiling, load‑aware scheduling, and differentiated SLO mixing, now fully integrated into Alibaba Cloud ACK with a new v1.1.1‑ack.1 release.
Shuhe Technology leveraged Alibaba Cloud Container Service with Knative and ECI virtual nodes to achieve auto‑scaling, multi‑version management, and up to 60% cost reduction for AI model services, dramatically improving resource efficiency and stability under burst traffic.
During the 2022 TT Voice Annual Summit, the technical team tackled stability, real‑time risk control, and resource utilization challenges by implementing strict change management, multi‑cloud high‑availability networking, AIOps‑driven monitoring, big‑data processing, and cloud‑native scaling strategies, ultimately delivering zero‑fault operation.
This article outlines ZuanZuan’s capacity management approach, covering its objectives, development stages, water‑level metrics, resource optimization techniques, cluster capacity assessment, stress‑test indicators and standards, as well as scaling strategies, demonstrating how systematic capacity management reduces costs while ensuring service stability.
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 case study details NetEase Cloud Music's full‑link baseline governance initiative, outlining the challenges of massive data pipelines, the metrics used to measure success, the three‑pronged action plan (infrastructure, task optimization, and standards), and the resulting improvements in availability, resource utilization, and monitoring accuracy.
Tencent’s shift from VMs to Kubernetes for massive online services faces pod‑size rigidity, heterogeneous node balancing, elastic scaling, and massive cluster‑pool mapping, prompting optimizations such as dynamic CPU compression, custom load‑aware scheduling, collaborative HPA/VPA scaling, dynamic quota migration, unified routing‑sync, and an automated decision‑tree‑driven self‑healing workflow for container‑destruction failures.
The article details Tencent Music's data governance journey, describing the background of rapid resource growth, challenges in cost management, a multi‑layered governance methodology—including metadata, tiered storage, and a Lego metadata platform—and the resulting improvements in resource utilization and data quality.
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.
NetEase Media migrated SparkSQL to Kubernetes in 2021, using storage‑compute decoupling, hybrid deployment, custom scripts, Kyuubi failover, and extensive monitoring and resource governance, which cut cluster size by over 30% while keeping CPU utilization above 80% and GC throughput above 95%.
This article details Baidu's multi‑step Android APK resource‑size reduction strategy, covering the structure of APK resources, built‑in AAPT2 optimizations, custom path and filename shortening, resource‑name collapsing, sparse entry encoding, image compression, duplicate‑resource handling, and the impact of AAB and Jetpack Compose on overall package size.
The article details Baidu App’s third‑stage Android APK size reduction, explaining the res/ and assets structure, reviewing AGP’s OptimizeResources, ShrinkResources and new flags, and describing a custom AAPT2‑based tool that shortens paths and filenames, removes extensions, collapses names, uses sparse encoding, integrates third‑party guards and image‑compression techniques to significantly shrink the package.
This article details a comprehensive, step‑by‑step approach to reducing Android APK size—including library pruning, resource compression, class file optimization, and automated monitoring—to keep app packages lightweight and prevent size regression over successive releases.
The article surveys GPU container virtualization for AI heterogeneous computing, detailing utilization challenges, historical architectures, various virtualization methods, Baidu's dual-engine user- and kernel-space design with isolation and scheduling features, performance benefits, best‑practice scenarios, and deployment guidance, concluding with a technical Q&A.
This article explores Baidu’s cutting‑edge GPU container virtualization architecture, detailing the challenges of low GPU utilization in AI workloads, the dual‑engine (user‑space and kernel‑space) isolation mechanisms, various mixing strategies, performance evaluations, and best‑practice recommendations for maximizing resource efficiency in large‑scale AI deployments.
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.
This article describes the challenges encountered when migrating tens of thousands of Kubernetes cronjobs from VMs to a cluster—node instability, low resource utilization, and scheduling delays—and explains how introducing a serverless architecture with virtual nodes, a custom job scheduler, unified logging and monitoring, and sandbox reuse restored stability, improved performance, and reduced resource costs by about 70%.
This article outlines practical strategies for organizing, reducing, and testing art assets in game development, covering resource reference standards, systematic reduction workflows, timely management of seasonal assets, detailed QA testing guidelines, and solutions to common testing challenges.
Koordinator, an open‑source cloud‑native scheduler born from Alibaba's massive production practice, enables efficient, low‑cost mixed‑workload orchestration on Kubernetes by addressing integration challenges, offering a resource‑oversell model, and providing zero‑intrusion deployment for both cloud and on‑prem environments.
This article presents a comprehensive approach to optimizing Log4j2 logging in high‑traffic Java services by reducing disk and CPU consumption through log level control and pattern tuning, adopting asynchronous logging, and enabling end‑to‑end request traceability using ThreadLocal, TransmittableThreadLocal, and MDC techniques, supported by performance test results and practical recommendations.
This article details TAL Education's systematic approach to improving container CPU utilization and reducing cloud expenses through dynamic scaling, resource overcommit strategies, mixed online‑offline deployments, and careful selection of public‑cloud compute types, supported by real‑world data and best‑practice recommendations.
The “East Data West Computing” program is a national strategy that relocates computing workloads from data‑intensive eastern regions to resource‑rich western areas by building a network of data‑center hubs and clusters, aiming to balance supply and demand, improve energy efficiency, and boost overall computing capacity.
The article details how Tencent's Happy Game Studio transformed its large‑scale, low‑utilization backend from a legacy distributed architecture to a cloud‑native, Istio‑enabled service‑mesh platform, achieving significant resource savings, smoother deployments, and improved observability across game, CGI, and storage services.
Baidu’s search content platform transitioned to a serverless, FaaS‑based architecture with intelligent scheduling and automated control, cutting resource waste by 87%, boosting automatic recovery to 96.7%, and delivering roughly tenfold productivity gains across development, deployment, and maintenance while simplifying scalability and high‑availability concerns.
This article details the 58.com WPAI machine learning platform's architecture and the optimizations applied to training task scheduling, inference service elastic scaling, and offline‑online resource mixing, demonstrating how these techniques significantly improve GPU/CPU utilization and inference performance across both GPU and CPU environments.
This article examines why IT talent is the most expensive resource, analyzes environmental, temporal, and human factors, and proposes a structured change management framework—including clear methodology, strong leadership support, and iterative improvement principles—to optimize IT resource allocation and reduce costs.
This article describes Beike's real‑time computing team's end‑to‑end practices for collecting and storing Flink metrics, building visual monitoring dashboards, implementing multi‑level alerting, analyzing logs, estimating CPU and memory resources, and deploying Flink on Kubernetes with containerization and storage separation to improve stability, resource utilization, and operational efficiency.
This talk outlines the evolution of Kuaishou's video infrastructure—from its early service‑oriented design through workflow‑engine and FaaS upgrades, to fine‑grained ROI‑driven resource management and infrastructure promotion, highlighting lessons shared with Netflix and Facebook and future functional‑declaration directions.
This case study outlines how a cross‑functional design team re‑examined meeting‑room usage, identified low utilization, set clear product goals, implemented shorter reservation slots, room‑grab features, richer room details, and system integrations, resulting in a 26% rise in daily bookings and improved efficiency.
This article details Meituan Waimai's data warehouse evolution—covering business context, four‑layer architecture, Spark‑based ETL, successive V1.0, V2.0, and V3.0 redesigns, data governance practices, resource‑optimization tactics, security measures, and future road‑maps—illustrated with diagrams and concrete technical choices.
This guide explains Kubernetes capacity planning, showing how to detect idle CPU and memory, identify wasteful namespaces, use open‑source tools like kube‑state‑metrics and cAdvisor, and apply PromQL queries to optimize resource requests and measure the impact of your improvements.
This article explains how to improve Kubernetes resource utilization by addressing node fragmentation, pod misconfiguration, and HPA settings, and then details five concrete scheduling methods—including nodeName, nodeSelector, nodeAffinity, taints, and pod priority—to optimize pod placement and reduce waste.
This article explores practical methods for optimizing Flink real‑time task resources on Kubernetes, focusing on memory usage analysis via GC logs and message‑processing capacity assessment, proposing automated detection of over‑provisioned memory and CPU, and outlining a workflow for resource adjustment to reduce costs.
At Youzan, Flink real‑time tasks running on Kubernetes are optimized by daily GC‑log memory analysis and Kafka‑throughput monitoring, which compute recommended heap sizes and parallelism adjustments to eliminate over‑provisioned CPU and memory, automate alerts, and pave the way for fully automated resource tuning.
Meituan’s autonomous vehicle engine abstracts communication, scheduling, data handling, and tooling into three layers to ensure deterministic behavior across on‑vehicle and simulation environments, tackling consistency, scheduling, and resource‑utilization challenges by using unified computation models, distributed graph deployment, caching, and remote model serving, thereby accelerating autonomous delivery vehicle development.
The article analyzes the limitations of native Kubernetes scheduling, proposes usage‑based and dynamic‑weight scheduling strategies to improve multi‑resource balance, presents experimental results showing significant CPU and memory utilization gains, and discusses cost benefits and future directions for the private cloud platform.
The Gaode Smart Scenic Spot Pocket‑Player framework provides a cross‑platform, hybrid audio solution that delivers location‑based MP3 guides, isolates business logic via an observer‑based service, manages audio focus and interruptions efficiently, supports cross‑page and offline playback, and enables rapid, low‑resource development for immersive tourist experiences.
The 2020 JD.com 618 shopping festival showcased a record-breaking sales volume and demonstrated how meticulous resource fine‑tuning, container scaling, high‑fidelity load testing, and cross‑team coordination enabled stable, cost‑effective system performance without adding new compute servers.
This article explains how to analyze and reduce the size of iOS .ipa packages by classifying resources, using LSUnusedResources to identify unused files, isolating business‑line assets via podspec subspecs, and automating image compression with tools like ImageOptim, TinyPNG and pre‑commit Git hooks.
This article details Ctrip Cloud Container's practical experience in building a unified, policy‑driven scheduling framework for Kubernetes, covering algorithm parameterization, affinity configuration, extended resource‑balancing, load‑aware scoring, and performance tuning that together raise scheduling throughput by over five times in large‑scale clusters.
This article explains how to use Python to analyze Android APK packages, extract basic statistics, identify optimizable resources such as oversized images, duplicate files, and unused assets, and provide data‑driven guidance for reducing APK size and improving distribution efficiency.
This article explains how classic NES titles like Super Mario manage to fit all code, graphics, music and level data within a mere 128 KB by exploiting 2‑bit palettes, minimalist MIDI audio, tile reuse and hand‑crafted assembly, revealing a storage gap of five to seven orders of magnitude compared to modern games.
The talk presented a comprehensive strategy for boosting Kubernetes cluster efficiency by addressing fragmentation, over‑provisioned pod requests, and misaligned autoscaling through five techniques—pod compression, node over‑commit, HPAPlus, VPAPlus, and dynamic scheduling—implemented via mutating admission controllers, Prometheus analytics, and extended autoscaler components.
This article examines Xiaomi's migration from Spark Streaming to Apache Flink, comparing scheduling strategies, mini‑batch versus true streaming, resource utilization, latency, and serialization mechanisms, and concludes with practical insights and custom optimization techniques for large‑scale data processing.
Alibaba's CCO team built XSigma, an AI‑driven, automated customer‑service scheduling platform that tackles unpredictable call volumes, improves agent utilization, and enhances user experience through predictive staffing, load‑balancing, intelligent matching, and real‑time visual monitoring.
This article explains how Alibaba's MaxCompute platform tackles capacity bottlenecks by combining data‑driven insights, linear programming, and automated project migration strategies to predict resource needs, optimize cluster allocation, and quantify migration impacts for improved operational efficiency.
This article details JD.com's database automation journey, covering their intelligent database management platform (DBS), automated deployment systems, intelligent analysis and diagnostics, and the evolution from traditional containerization to ContainerDB, a smart database service with elastic scheduling and resource optimization.
