The article summarizes Alibaba Cloud's 2024 conference talks on AI‑enhanced observability, presenting a cloud‑native GitOps solution for massive clusters and showcasing large‑model applications in intelligent Q&A and diagnosis to improve operational stability, cost, and efficiency.
Xiaohongshu’s AIOps initiative builds a four‑layer framework that leverages machine‑learning‑driven anomaly detection, causal analysis, and trace‑based fault localization to automatically identify root‑cause services in micro‑service environments, achieving over 80 % accuracy across 1000 daily diagnoses while guiding future enhancements in change correlation and automated remediation.
This article explores the progression of IT operations—from manual processes through automated DevOps, to AI‑driven AIOps and chat‑based ChatOps—examining concepts, advantages, tools, and future possibilities, while also reflecting on how these trends reshape the role of operations engineers.
The 2024 XOps Forum in Beijing showcased a new era of smart operations, unveiling a record‑breaking AIOps survey that highlights rapid investment growth, rising adoption of large language models, evolving maturity levels, and key challenges such as model accuracy and data quality across Chinese enterprises.
The article summarizes ByteDance's intelligent operations (AIOps) strategy, covering frontier concepts, a five‑level automation roadmap, large‑model applications for fault diagnosis and smart Q&A, and a comprehensive AIOps platform that accelerates algorithm deployment, improves efficiency, and reduces operational costs.
This article reviews the transformation of IT operations from manual processes to automation, AIOps, and ChatOps, and explains how large language models can further enhance intelligent monitoring, automated diagnosis, and knowledge‑base assistance to improve system stability and response speed.
From manual server management to automated scripts, AIOps, and ChatOps, this article traces the evolution of IT operations and demonstrates how large language models boost efficiency, enable intelligent assistants, automated diagnostics, and smart log analysis, aiming for rapid fault detection, localization, and resolution.
This article explores the latest concepts, planning roadmap, and practical applications of large‑model AI in intelligent operations, detailing AIOps use cases, system‑level automation, multi‑agent architectures, and how a dedicated platform accelerates deployment and efficiency across data‑center environments.
The paper describes Baidu’s AI‑native advertising platform that employs a multi‑agent architecture built on large‑language models—combining large‑small model collaboration, domain SOP‑driven coordination, and long‑term memory—to enable natural‑language understanding, proactive planning, execution and human‑like responses, illustrated by GBI analytics and JarvisBot operations, delivering higher consumption, accuracy, speed and efficiency.
At the 2024 XCOPS Intelligent Operations Management Annual Meeting in Guangzhou, Vivo’s Deng Song will discuss building a robust database operations platform, addressing availability threats, efficiency levers, 0‑to‑1 development strategies, and considerations of reliability, cost, and data privacy amid emerging AI and large‑model technologies.
This article explores why large‑model Agent technology is essential for AIOps, explains single‑ and multi‑Agent architectures, memory and tool integration, and demonstrates practical applications such as anomaly detection, fault diagnosis, automated remediation, ChatOps, and future directions for intelligent, autonomous operations.
The presentation explains how large‑model agents empower AIOps by automating routine tasks, enhancing anomaly detection, fault diagnosis, and remediation, while outlining architectural components, multi‑agent collaboration, and future directions for building self‑healing, observability‑driven operations platforms.
AIOps leverages AI to automate IT monitoring, predict failures, and optimize resources, offering modern enterprises reduced operational workload and higher reliability, while facing challenges such as data governance, automation, hierarchical monitoring, and large‑model hallucinations that must be addressed for successful deployment.
The 2023 China DevOps Survey reveals that telecom operators are rapidly advancing their DevOps maturity, with over 60% achieving comprehensive maturity, driven by standardized models, continuous delivery practices, and extensive industry assessments across more than twenty sectors.
This article explains how applying machine learning to database operations—specifically AIOps for MySQL—can automate index recommendation by parsing SQL, extracting semantic and statistical features, generating candidate index combinations, and training an XGBoost model to predict optimal indexes, reducing reliance on manual DBA work.
This article explains how AIOps leverages AI, machine learning, and dynamic threshold techniques to handle massive, multimodal monitoring data, improve anomaly detection, and enhance IT operation reliability through metric classification, baseline prediction, and automated fault remediation.
This article details Ant Group's practical implementation of Service Level Objectives (SLO) and AIOps to achieve fine‑grained operations, covering SLO fundamentals, health‑score architecture, GitOps‑based data pipelines, error‑budget alerting, AI‑driven anomaly detection, fault localization techniques, and real‑world case studies on dashboards, Kubernetes SLOs, and emergency response workflows.
This article details Meituan's two‑year exploration of AIOps for incident management, covering the challenges of massive, real‑time operational data, the AI‑driven modules for risk prevention, fault detection, diagnosis, and similar‑incident recommendation, and future directions such as intelligent log detection and change recognition.
The 21st GOPS Global Operations Conference in Shanghai featured Shenwan Hongyuan Securities' Yusi Song presenting an in‑depth look at automated operations, covering achievements, experience summaries, and future plans, with slide images and a downloadable PPT for attendees.
In the context of digital transformation, six leading Chinese telecom operators applied the CAICT DevOps Capability Maturity Model to evaluate dozens of projects, achieving significant improvements in continuous delivery, technical operations, security, and AIOps, providing valuable references for the industry.
Amid China's sweeping digital, networked, and intelligent transformation, over 100 leading enterprises across banking, finance, communications, manufacturing, and other sectors have participated in DevOps and AIOps maturity model evaluations, providing a comprehensive view of industry adoption, capability levels, and emerging best practices for 2023.
This article presents Meituan's two‑year exploration of AIOps in incident management, detailing risk‑prevention change detection, real‑time anomaly discovery, automated root‑cause diagnosis, multi‑dimensional KPI analysis, and similar‑event recommendation, while sharing architectural designs, algorithmic techniques, performance results, and future directions.
Amid China's sweeping digital transformation, the China Academy of Information and Communications Technology (CAICT) reports that 104 leading enterprises across banking, securities, insurance, telecom, manufacturing and other sectors have completed 336 DevOps maturity assessments and 23 enterprises have finished 45 AIOps assessments in 2023, highlighting industry‑wide adoption of DevOps and AIOps standards and offering detailed breakdowns by sector, evaluation levels, and future guidance.
This article details how the Export‑Import Bank of China built an AIOps‑driven operations risk compliance platform to address limited monitoring, data silos, low automation, and security gaps, outlining its architecture, technical methods, three key monitoring scenarios, and measurable benefits.
The article explains the ITU‑T Y.3550 AIOps standard, its AI‑driven cloud service development and operation requirements, the Chinese AIOps maturity‑model series, and the latest assessment results showing dozens of enterprises adopting these intelligent‑operations capabilities.
Meituan’s service‑operations team applies AIOps across prevention, detection, and post‑incident stages—using change‑risk analysis, real‑time graph‑based anomaly detection, similarity‑driven root‑cause diagnosis, and NLP‑powered incident recommendation—to achieve sub‑second detection, high precision, 28% faster fault handling, and plans for intelligent log and change recognition.
Mobile Cloud’s “CC‑OPS” platform recently passed the CAICT AIOps Level‑3 General Capability assessment, marking the first cloud provider to achieve this rating and showcasing how AI‑driven operations can boost system stability, efficiency, and cost savings across enterprises.
The article details Mobile Cloud's successful third‑level AIOps assessment by the China Information and Communication Academy, explores the platform's architecture and intelligent operation capabilities, shares interview insights on challenges, benefits, and future plans, and presents industry‑wide AIOps maturity statistics.
Bilibili revamped its alert monitoring platform to meet rapid growth, focusing on effectiveness, timeliness, and coverage; it introduced a closed‑loop design and governance that cut weekly alerts by 90%, built a knowledge‑graph root‑cause system achieving 87.9% accuracy with sub‑minute latency, and integrated AIOps for ongoing refinement.
The article traces the evolution of automation from ancient myths and the printing press to modern AI‑driven database management, showing how AIOps and automated tools boost productivity, free IT teams from repetitive tasks, and enable more innovative work across organizations.
This article examines the challenges of big‑data operations, explains how DataOps and AIOps complement each other, and details the ABM intelligent operations architecture, platform components, and real‑world use cases such as Flink hotspot detection, ChatOps assistants, and dynamic MaxCompute resource optimization.
This article explains what intelligent operations (AIOps) are, outlines a four‑layer platform architecture, and showcases real‑world practices such as load‑balancing link repair, MySQL container self‑healing, composite service tracing, component‑based orchestration, and AI‑driven log analysis, concluding with future prospects.
This article examines the rapid growth of the Chinese IT operations market, explains why AIOps faces trust challenges due to opaque deep‑learning models, and presents AsiaInfo's transparent‑model and post‑hoc explanation engine together with three concrete explainable root‑cause analysis methods, concluding with future outlooks for trustworthy AIOps.
At Didi, observability and stability engineering combine automated, AI‑driven alarm generation, distributed tracing, and ChatOps‑based fault handling to manage micro‑service complexity, massive traffic spikes, and cross‑region operations, emphasizing systematic investment, AIOps evolution, and a recruitment call for backend and test engineers.
The article describes Ctrip's AIOps approach to improving alert quality by combining statistical methods and machine‑learning models such as LSTM, trend analysis, adaptive threshold calculation, and dynamic‑time‑warping based periodic anomaly detection, achieving significant gains in precision and fault‑recall rates.
The recent release of China Academy of Information and Communications Technology's DevOps, AIOps, and Identity Governance maturity model assessment results showcases extensive industry adoption, highlights the impact of standardization and tool empowerment on digital transformation, and provides detailed statistics across banking, securities, insurance, telecom, and other sectors.
Didi’s ride‑hailing R&D team addresses efficiency and stability challenges of a large micro‑service ecosystem by unifying a Go stack, common framework, and data models, using eBPF traffic recording for automated regression testing, and applying AIOps alert filtering, knowledge‑graph root‑cause analysis, and a localization robot for rapid fault recovery, while targeting full CI/CD automation with static analysis, service‑mesh observability, and chaos engineering.
This article outlines the evolution from manual operations to DevOps and SRE‑driven “big operations,” detailing system reliability and continuity practices, observability concepts, and the development of AIOps maturity standards, offering a comprehensive guide for building stable, efficient, and secure operational frameworks.
The article outlines how AI, especially large language models and ML‑DevOps, is reshaping software engineering from historical roots through requirement mining, design automation, intelligent coding, testing, and AIOps, culminating in the transformative impact of GPT‑4 on development practices.
This article outlines the background, objectives, and implementation framework of AIOps at a major bank, detailing data consolidation, analysis engines, scenario ecosystems, practical case studies, and future directions for intelligent, proactive IT operations.
This article details Guangdong Mobile's evolution of IT systems and operations, explains the four domain architecture, chronicles the AIOps adoption timeline, showcases intelligent anomaly detection, change assessment, fault diagnosis, and operation robots, and shares practical promotion methods and future outlook for AI‑driven IT operations.
The 2023 China AIOps Status Survey, launched by CAICT and the Cloud Computing Open Source Industry Alliance, gathers input from over 60 enterprises to reveal current intelligent‑operations practices, observability adoption, generative AI prospects, and best‑practice case studies, while inviting participants to shape the upcoming report.
This article shares China Mobile IT Center's four‑year journey of designing, deploying, and refining a centralized AIOps platform that automates anomaly detection, fault diagnosis, and remediation, dramatically reducing complaint ticket handling from ten to six hours while scaling to billions of AI model calls per month.
This article presents a systematic overview of the emerging XOps ecosystem—including DevOps, BizDevOps, AIOps, FinOps, and SRE—detailing their relationships, maturity models, standards, and practical guidance for enterprises seeking to achieve efficient, secure, and data‑driven digital transformation.
Industrial and Financial Sectors’ first AIOps maturity assessment, led by China Information & Communication Research Institute, recognized Industrial Bank’s cloud‑native intelligent operation project as a domestic leader, detailing its architecture, performance metrics, interview insights, and future AI‑driven operational strategies.
This article summarizes Wang Xiaozheng’s 2023 China Data Intelligence Management Summit talk, outlining the challenges of operations transformation under cloud‑native, the core ideas behind digital ops, Zhejiang Mobile’s practical implementation across six "可" dimensions, and future outlooks for AIOps and metaverse‑driven collaboration.
This article analyzes the three main bottlenecks—stability, cost, and efficiency—encountered in large‑scale operations, presents a six‑stage pipeline and open‑source toolchain, and explains how cloud‑native technologies such as Kubernetes and AIOps can transform and automate massive infrastructure management.
This presentation outlines the observability ecosystem, the role and value of alerts within it, core functionalities of an intelligent alarm management platform, best‑practice recommendations, and a real‑world case study of deploying a unified observability solution for a large state‑owned investment group.
At the 20th GOPS Global Operations Conference in Shenzhen, Huawei expert Liu Yuliang outlined how AI and data can transform telecom network management from a network‑centric to a business‑centric, self‑driving model, highlighting key solutions such as ChatOps, EDNS, AABD Pro, and cross‑vendor topology reconstruction.
China's Information and Communication Research Institute announced the newest evaluation results for its DevOps and AIOps capability maturity models, showing that standardization and tool empowerment have helped over 75 leading enterprises across banking, securities, telecom, and internet sectors improve quality, efficiency, and market competitiveness.
The article explores how AIOps, a human‑machine collaborative approach powered by data, algorithms, and contextual knowledge, transforms modern operations by enabling real‑time insight, predictive decision‑making, automated execution, and continuous feedback, especially in complex, security‑sensitive environments like finance.
This article explains how NetEase Games leveraged AI, big data, and machine learning to create an AIOps platform that automates anomaly detection, log analysis, and fault localization, improving quality assurance, cost management, and operational efficiency across complex gaming infrastructures.
This paper presents StudRank, a microservice root‑cause localization method that decomposes call‑chain traces into time‑series, detects anomalies, builds an abnormal propagation graph, and applies a personalized PageRank random‑walk algorithm, achieving 84% top‑1 accuracy and a 97.6% improvement over MicroRCA on public AIOps data.
HoloInsight is an open‑source, cloud‑native observability platform derived from Ant Group's AntMonitor, offering integrated log‑based monitoring, business metric analysis, and AI‑driven AIOps capabilities while providing a lightweight, modular architecture and extensive extensibility for modern software stacks.
This article presents an AI‑enhanced operations framework that combines metric anomaly detection, alarm compression, log anomaly detection, and intelligent analysis using machine learning methods such as DBSCAN clustering, SARIMAX modeling, Apriori association rules, and LSTM‑based log parsing to improve fault detection and reduce operational costs.
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 reviews G Bank's multi‑year journey to standardize application monitoring, detailing the methodology, models, metrics, automation mechanisms, and quantitative evaluation that together improve visibility, early fault detection, and overall operations management for both traditional and distributed systems.
This article outlines Vivo's multi‑year journey of designing, evolving, and operating a cloud‑native, AIOps‑enabled monitoring platform that supports tens of thousands of hosts, databases, containers, and services, detailing its architecture, challenges, and future directions for observability and reliability.
This article explains the concept of AIOps, details China Mobile Information Technology's successful comprehensive‑level assessment of its centralized operations management platform's fault‑self‑healing module, shares insights from an interview with the project director, and introduces the national AIOps capability maturity model.
China Mobile Information Technology’s Management Information Domain Operation Management System passed the comprehensive-level AIOps fault‑prediction assessment, highlighting the growing importance of AI‑driven operations, the new AIOps capability maturity model, and insights from a project‑manager interview on future development.
This article explains the AIOps concept, details China Mobile Information Technology's award‑winning Disk Health Intelligent Detection System, and shares an interview with its cloud architect on the evaluation process, future plans, and the broader AIOps capability maturity model.
This article details China Mobile Information's interview about their centralized AIOps platform, the recent excellent‑level assessment by the China Academy of Information and Communications Technology, the system's key modules, future plans, and the broader significance of AI‑driven IT operations.
The article explains AIOps concepts, details China Mobile Information's award‑winning intelligent operations tools, shares an interview with deputy general manager Liang Enlei on their development, evaluation experience, future plans, and introduces the national AIOps maturity model and its key modules.
Guotai Junan’s Intelligent Operations Service Platform, powered by AI‑driven AIOps, passed the China Academy of Information and Communications Technology’s excellence assessment for anomaly detection, showcasing advanced data‑driven monitoring, digital‑transformation initiatives, and future plans for fault prediction, self‑healing, and comprehensive operations intelligence.
The article explains how AIOps—AI‑driven IT operations—has become a strategic trend, details Guotai Junan’s award‑winning intelligent operations platform that achieved the top‑level “exception detection” evaluation, and shares interview insights on implementation, challenges, and future directions.
The article describes a root‑cause localization algorithm implemented in vivo’s monitoring platform that automatically analyzes latency spikes by splitting service timelines, computing variance, clustering results with K‑means, and recursively tracing downstream services, achieving over 85 % accuracy for dependency failures while still requiring human verification and outlining future AI‑driven enhancements.
An interview with senior leaders of China Agricultural Bank reveals how their AIOps‑driven operations platform achieved an Excellent rating in the CAICT root‑cause analysis module, showcasing the bank’s intelligent operations strategy, implementation details, and future plans for expanding AI‑based monitoring across cloud and micro‑service environments.
The article details the evolution, architecture, capabilities, challenges, and future plans of Vivo's comprehensive monitoring platform, covering its transition from simple Zabbix setups to a cloud‑native, AI‑ops enabled system that ensures service availability across massive infrastructure.
The article outlines vivo’s evolution from simple Zabbix monitoring to a self‑built, unified monitoring platform that now covers infrastructure, containers, databases and user experience at massive scale, integrating AI‑ops, cloud‑native observability and unified alerting to ensure end‑to‑end service reliability and future intelligent, one‑stop monitoring.
AIOps combines AI techniques such as machine learning with data science to enhance IT operations, and the Chinese CAICT has released the first international AIOps capability maturity model, detailing evaluation criteria, modules like anomaly detection and root‑cause analysis, and announcing recent certified enterprises.
China Agricultural Bank’s R&D center shares its journey of passing multiple CAICT DevOps and AIOps maturity assessments, detailing the evaluated projects, metrics improvements, challenges overcome, and future plans, illustrating how standardized, tool‑enabled practices boost quality, efficiency, security, and digital transformation in a large financial institution.
The article introduces the AIOps (Artificial Intelligence for IT Operations) capability maturity model developed by China’s Information and Communication Research Institute, explains its two parts—general capabilities and system/tool technical requirements—lists the evaluated modules, and announces the upcoming certification ceremony and contact details for participation.
China Agricultural Bank’s recent DevOps and AIOps assessments, covering 17 projects across continuous delivery, security, application design, and intelligent operations, showcase how standardized processes, tool empowerment, and rigorous evaluation boosted efficiency, safety, and digital transformation, offering actionable insights for large enterprises seeking similar maturity.
This article details Tencent CDN's challenges and solutions for business continuity, covering bandwidth and device resource constraints, massive request handling, fault‑management lifecycle, automation bottlenecks, and the implementation of AIOps, intelligent alerts, capacity planning, and root‑cause analysis to ensure reliable service.
This article details Tencent CDN's extensive business continuity challenges—including bandwidth, device resources, and massive request volumes—and explains how a fault‑management lifecycle, AIOps components, intelligent alerting, and automated capacity planning together enable resilient, automated operations.
This article presents a comprehensive, three‑part guide on the current state of full‑stack operations monitoring, common challenges and solutions, and a real‑world use case, illustrating how Alibaba Cloud Elasticsearch can improve observability, boost performance, and cut costs for complex distributed systems.
Guoxin Securities’ QianKun centralized operation platform showcases a cloud‑native, micro‑service architecture that achieved 99.95% availability through containerization, multi‑region deployment, AI‑driven capacity forecasting, and comprehensive DevOps practices, offering a 24/7 seamless account‑opening experience and setting industry benchmarks.
This comprehensive guide outlines five common MySQL performance problems, a step‑by‑step investigation methodology, detailed Java middleware and database analyses, practical tuning tactics such as index optimization and sharding, and a governance framework for sustainable performance management.
The two‑day 2022 GOPS Global Operations Conference in Shanghai featured 16 tracks, over 80 speakers, new DevOps standards, extensive assessment results, and a wealth of sessions on DevOps, AIOps, cloud‑native practices, security, and industry case studies, offering a comprehensive snapshot of modern operations engineering.
China Mobile Cloud’s Intelligent Operations (AIOps) platform passed the CAICT comprehensive evaluation, reaching industry‑leading L2 maturity, and the interview reveals the project’s goals, outcomes, and future plans for advancing AI‑driven IT operations.
This article examines the data challenges faced by intelligent operations (AIOps), evaluates IoTDB against other time‑series databases through performance benchmarks, outlines Cloudwise's architecture and open‑source contributions, and presents real‑world case studies demonstrating anomaly detection and root‑cause analysis in industrial settings.
This article presents a comprehensive overview of an event‑centric intelligent alert analysis platform, covering its evolution, core challenges, the concept of alert events, AI‑driven correlation techniques, and the MC‑Stack platform that powers modern operations.
The article examines how rapid AI adoption, AIOps, and hybrid‑work infrastructure investments are reshaping enterprise networking, security, and automation, highlighting AI‑powered assistants that can troubleshoot networks, the rise of SASE, and future trends toward human‑level AI performance.
This article describes NetEase's AIOps journey for game operations, explaining the Gartner definition of intelligent operations, the implementation roadmap, detailed anomaly‑detection techniques for business, performance, and log data, and a comprehensive fault‑localization workflow that combines resource, code, and historical analysis.
This article details Huolala's journey from basic monitoring to an AI‑driven intelligent observability platform, covering AIOps concepts, a comprehensive monitoring framework, practical implementations, automated alert analysis, lessons learned, and future directions for large‑scale operations.
ICBC’s software development center outlines its multi‑year journey adopting XOps practices—DevOps, DevSecOps, DataOps, MLOps, AIOps, ChatOps and BizDevOps—to boost development efficiency, enhance security, accelerate data‑driven AI, and cut costs, showcasing measurable improvements in release frequency, defect rates, and operational automation.
In this interview, senior database expert Kong Zaihua explains the main performance, functional, and usability shortcomings of domestic Chinese databases compared with Oracle, and outlines practical strategies, tools, and migration techniques to evaluate compatibility, handle DDL conversion, manage online/offline data transfer, and reduce reliance on stored procedures.
The 2022 inaugural XOps Industry Ecosystem Summit in Beijing gathered over six hundred thousand viewers to explore digital governance, DevOps, FinOps, AIOps and related standards, featuring keynote speeches, forum discussions, assessment result releases, and the launch of the Digital Governance Alliance and FinOps alliance.
At the 2022 XOps Industry Ecosystem Summit in Beijing, the China Academy of Information and Communications Technology unveiled the "2022 XOps Top Ten Keywords", highlighting trends such as DevOps, DevSecOps, CO‑DevOps, Continuous Testing, R&D efficiency metrics, AIOps, BizDevOps, FinOps, ArchOps and Digital Governance that are driving the evolution of modern IT operations.
The article reports on the 2022 XOps Industry Ecosystem Summit hosted by CAICT, announces the results of multiple DevOps and AIOps capability maturity model assessments across dozens of leading enterprises, explains the standards' origins and scope, and provides contact details for further evaluation inquiries.
This article details Huolala's journey from a fragmented monitoring stack to a unified, AI‑enhanced observability platform, covering AIOps concepts, the design of a comprehensive monitoring framework, concrete implementation of metrics, tracing, logging, alerting, and lessons learned for large‑scale operations.
The article details Ant Group’s AntMonitor observability platform, covering its development timeline, holographic monitoring capabilities, integrated performance analysis, efficient data integration, built‑in AI‑driven analytics, Monitoring‑as‑a‑Service, and the underlying high‑performance time‑series database and cloud‑native architecture that support massive real‑time data processing.
This article summarizes Professor Niu Xiaoling’s GNSEC 2022 keynote, outlining the XOps framework and its five pillars—XOps, DevOps research and operational efficiency metrics, AIOps, BizDevOps, and FinOps—while detailing their drivers, maturity models, implementation examples, and the role of standards in guiding enterprises toward intelligent, cost‑effective, and business‑value‑focused software delivery.
The article explores how aviation‑grade monitoring concepts—such as multi‑level alarm classification, diverse alert delivery methods, and comprehensive sensor coverage—can inspire centralized, data‑driven IT operations monitoring architectures that reduce missed alerts, false positives, and improve response times.
The 2022 online AIOps Technology Salon hosted by the China Academy of Information and Communications Technology gathered over 13,000 viewers, featured expert talks on standards, practical implementations, anomaly‑detection algorithms, and real‑world case studies from major enterprises, offering actionable insights for modern IT operations.
The article introduces the 2022 China AIOps Current Situation Survey, explains how rapid data growth and digital transformation are driving intelligent operations, outlines the joint effort of over 60 enterprises to compile the report, and invites industry professionals to contribute their insights through an online questionnaire.
China’s AIOps industry, highlighted in the 2022 “China AIOps Status Survey” led by CAICT and major telecom and cloud firms, examines the rise of intelligent operations since Gartner’s 2016 definition, detailing its role in digital transformation, infrastructure upgrades, and a detailed five‑month research timeline.
The article explains the rising importance of observability in modern operations, defines its control‑theory roots, breaks it down into metrics, traces and logs, and argues that successful implementation requires three pillars—SRE practices, AIOps algorithms, and deep business‑architecture knowledge—together with well‑designed SLOs and critical‑path mapping.
MDAP is a multi‑dimensional real‑time monitoring platform for mobile apps that gathers metrics, logs, and traces via lightweight SDKs, processes data through micro‑service back‑ends using Flink, Spark, and ClickHouse, applies intelligent analysis for smoothness scoring, memory‑snapshot optimization, stack de‑obfuscation, crash clustering, and URL templating, and aims to extend end‑to‑end observability and predictive issue detection.
This article examines the challenges of intelligent telecom network operation, presents Zhejiang Mobile’s AI‑powered self‑healing practice—including process re‑design, system reconstruction, talent transformation, and measurable results—and outlines the AIOps maturity model and future outlook for digital network management.
