This solution tackles the challenge of measuring uncertainty in job duration predictions through a structured two-step process.

In Step 1, it evaluates the similarity of incoming jobs compared to those utilized during training by employing advanced similarity assessment techniques. This assessment helps identify atypical jobs that may suggest lower confidence in the generated predictions.

In Step 2, the solution integrates methods for quantifying prediction uncertainty using sophisticated statistical techniques. The approach involves training multiple models and analyzing their variance to accurately assess reliability. Additionally, it incorporates probabilistic modeling techniques that not only predict outcomes but also provide estimates of the associated uncertainties.

This comprehensive strategy equips users with a clearer understanding of prediction reliability, which is critical in HPC environments where the confidence in estimates significantly influences resource allocation and job scheduling decisions.