IT Operations Automation
Chanjet needed user-centric observability and faster incident handling for a multitenant, multi-center SaaS platform serving millions of small businesses. Its traditional monitoring stack could not detect customer-experience issues early, alert storms buried urgent incidents, and emergency response still depended too heavily on expert operator judgment.
Reported impact
Chanjet raised overall service availability from 99.9% to 99.995%, cut fault-location time from more than 10 minutes to under 30 seconds, improved the MTTK step by more than 20x, and reached its target of preventing 99% of incidents before users are affected while shifting daily operations toward an AI-first, human-review model.
Chanjet and Alibaba Cloud rebuilt operations around a five-layer observability model and an AI-driven STAROps control plane. Infrastructure, middleware, application, business, and end-user experience signals are collected into the Cloud Monitor 2.0 stack, where Cloud Monitor CMS, Log Service SLS, and Application Real-Time Monitoring Service ARMS provide the shared telemetry base. STAROps then uses UModel-based digital-twin topology to correlate applications, resources, tenants, and call chains so the team can move from a single user-facing alert to the affected instance, dependency, and tenant. On top of that data layer, Chanjet deployed AI inspection, alert deduplication, diagnosis, self-healing, capacity prediction, and configuration validation workflows, with tickets and recovery actions flowing through standardized runbooks instead of ad hoc operator memory. The result is an AI-first operating model with human review for high-risk actions.
Infrastructure, middleware, application, business, and user-experience telemetry is ingested by CMS, SLS, and ARMS, correlated into application-resource-tenant topology views in STAROps, and then fed into AI inspection, diagnosis, alert-reduction, and self-healing workflows that create tickets, notify staff, and trigger recovery actions.
Chanjet wanted to raise overall SLA from 99.9% to 99.995%, achieve 99% pre-incident prevention, and finish emergency containment within 10 minutes (original: 畅捷通明确升级目标：将整体服务可用性 SLA 从 99.9% 提升至 99.995%，构建 99% 故障事前预防、10 分钟内完成应急止损的运维能力).
Cloud Monitor 2.0 unifies SLS, ARMS, CMS, and STAROps so Chanjet can aggregate, store, and analyze five layers of monitoring data on one platform (original: 云监控 2.0 将阿里云上的日志服务 SLS、应用实时监控服务 ARMS、云监控 CMS、全域智能运维平台 STAROps 这几个产品整合到一个平台中).
Overall SLA rose to 99.995%, fault-location time fell from more than 10 minutes to under 30 seconds, and MTTK efficiency improved by more than 20x (original: 整体 SLA 从 99.9% 提升至 99.995%... 故障定位时间从平均 10 分钟以上压缩至 30 秒以内，MTTR 中的 MTTK 环节效率提升超过 20 倍).
The final AI operating model adds intelligent inspection, alerting, diagnosis, self-healing, capacity prediction, and configuration validation so work shifts from human-led to AI-led with human review (original: 在平台化和方法论双重基础上全面叠加 AI 能力，工作模式从“以人为主”彻底转向“AI 为主、人审核”).
UModel organizes cloud products, Kubernetes resources, applications, and enterprise extensions into one semantic model so operators can drill down from a single user-experience alert to the affected instance, resource, and tenant (original: UModel 以统一图模型组织实体、关系、观测数据和运维知识... 运维人员可从单条用户体验告警逐层下钻，快速定位异常实例、资源瓶颈及受影响租户).
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Scoring methodology
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Traceability
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