Feifei Li (Alibaba Group)
Cloud database proactively adopts AI and ML techniques in the enterprise cloud database usage scenarios. This talk summarizes our practice to conduct fundamental research with an aim for adoption in cloud database production environment. AI and ML techniques can be used to improve both DevOps and DB kernels. For the DevOps scenario, we launched Database Autonomy Service (DAS), which is based on observability to improve cloud database usability, such as anomaly detection, root cause identification, drill down analysis, SQL query optimization, diagnosis via DB knowledge base, and resource optimization and autoscaling. For the intelligent database kernel scenario, we provide built-in AI computations through declarative SQL statements to reduce data movement and simplify the development cycle of applying AI solutions. We present our recent work on improving DB performance through knob tuning and identifying hot data for tiered DB storage based on survival analysis as two specific examples.
