The solution presents a dynamic strategy aimed at optimizing execution time predictions within high-performance computing (HPC) systems.

In Step 1, the model is embedded into a prediction pipeline utilizing a custom loss function that emphasizes reducing underestimations. This approach encourages the model to be conservative in its predictions, ensuring that greater losses are calculated for underestimations when the actual execution times exceed the anticipated durations.

In Step 2, the strategy involves adjusting the predictions based on a variety of input factors, including the confidence level of the model and user-provided estimates. It underscores the importance of recommending realistic wall times to mitigate the risks associated with job failures and to reduce resource wastage.

This is achieved through a dynamic adjustment mechanism that thoughtfully combines predicted values with appropriate adjustment factors. The overall goal of this methodology is to prevent premature job terminations, alleviate user frustration, and ultimately enhance the efficiency of job processing queues.