How China Mobile Cloud Achieved Leading‑Edge AIOps Maturity

Intelligent Operations (AIOps) applies artificial intelligence techniques such as machine learning and data science to IT operations problems, enhancing and partially replacing core IT operational functions. Gartner describes AIOps as extracting and analyzing ever‑growing volumes, varieties, and velocities of IT data in a loosely coupled, scalable manner to support IT operations management products.

On October 28, 2022, the China Academy of Information and Communications Technology (CAICT) announced the latest assessment results for DevOps and AIOps standards. China Mobile Cloud Capability Center’s “Mobile Cloud Smart Operations Platform” successfully passed the CAICT evaluation of the "Cloud Computing Intelligent Operations (AIOps) Capability Maturity – Part 2: System and Tool Technical Requirements". Its anomaly detection module achieved a comprehensive‑level assessment, indicating the platform has reached industry‑leading AIOps L2 maturity.

Interview with Zhu Wenpeng, Deputy General Manager of SRE/Security at China Mobile Cloud Capability Center

Q: Please introduce your organization and the project evaluated. Zhu explained that China Mobile Cloud Capability Center, a wholly‑owned subsidiary of China Mobile, provides cloud services nationwide, covering the entire cloud computing industry chain and offering solutions for government, healthcare, education, transportation, manufacturing, finance, and more. The evaluated project is the "Mobile Cloud Smart Operations Platform" focusing on the anomaly detection scenario within the AIOps capability maturity model, leveraging operational data and AI algorithms to drive digital transformation, ensure business continuity, and improve overall efficiency.

Q: How do you feel about passing the AIOps capability maturity assessment? He noted that AIOps is the future of operations and that the "Intelligent Operations Capability Maturity Standard" provides a clear implementation path, accelerating digital transformation in the operations domain.

Q: What considerations drove your participation in the assessment? The team aimed to identify gaps with the standard, understand their current AIOps status, discover issues, and use the assessment to promote building, improvement, governance, and development, while learning from industry best practices.

Q: What changes has the assessment brought to your team? The systematic evaluation validated the design philosophy, captured experience in building a standards‑compliant intelligent operations platform, and provided guidance for the next evolution of the platform. It also enhanced team members' capabilities and confidence in intelligent operations.

Q: What are the next steps for AIOps work? Building on the successful "anomaly detection" scenario (L2), the team will continue to solidify other scenarios such as dynamic baselines, capacity forecasting, and root‑cause analysis, explore advanced intelligent capabilities, and pursue L3‑level intelligent operations to improve efficiency.

Q: What is your view on the future development of AIOps? Zhu highlighted that most current use cases focus on traditional cloud service fault diagnosis and resource operation. With the emergence of compute networks, there is no universal solution yet, and the SRE team hopes to collaborate with industry peers and research institutions to develop an AIOps framework that supports future compute‑network operations.