How the Cloudflare global network optimizes for system reboots during low-traffic periods

The author discusses how they developed a system that uses curve fitting techniques from the field of signal processing to determine maintenance windows for their servers. They use sine wave models to fit the observed CPU utilization patterns over time and extract information about periodicity, amplitude, phase, and offset. This allows them to predict when it would be safe to perform server reboots without disrupting service availability. The system is implemented in Python using the `curve_fit` function from SciPy's optimization module. They also calculate a goodness of fit measure based on chi-square statistics to assess the accuracy of each fitted sine wave model. This approach enables them to automate server reboots and optimize resource utilization while minimizing disruptions in service availability. Question: How does the author ensure that the chosen maintenance window is accurate?