Predicting the Impact of Job Placement on System Performance for z/OS: A Machine Learning Approach
Vivek MISHRA · Digital Repository (National Repository of Grey Literature) · 2024
Efficient utilization of large-scale HPC systems, including z/OS, prioritizes resource management and job scheduling. Modern job scheduling systems need estimates of total wall time and CPU time at job submission to make reliable scheduling decisions. However, z/OS lacks a method for specifying these estimates at submission. This thesis investigates the impact of different job metadata on wall and CPU time prediction using simple conditional-driven programming, machine learning methods. Results indicate that for z/OS, complex machine learning models perform as well as simple condition-driven programming based on job names.