In our last blog post, we talked about how to track Mainline Linux Kernel Performance in Cloud Environments. In this blog, we examine the architecture of a working Linux kernel performance regression detection system: how payloads are selected and test plans generated, how target kernels are built and benchmarks executed, and how results are processed into reproducible regression signals and visualised. They must be ingested, normalized, and processed into structured data before any regression decision can be made. Instance-level comparison: Direct comparison of raw benchmark outputs, logs, and environment metadata between baseline and target. He develops systems that automate engineering workflows and data analysis, and support regression investigation and root cause analysis.