Abstract
Mainframe system processing includes a “Batch Cycle” that approximately spans in regular interval on a daily basis. The core part of the cycle completes in the middle of the regular interval with key client deliverables associated with the end times of certain jobs are tracked by service delivery. There are single and multi-client batch streams, a QA stream which includes all clients, and about huge batch jobs per day that execute. Despite a sophisticated job scheduling software and automated system workload management, operator intervention is required. The outcome of our proposed work is to bring out the high priority job first. According to our method, the jobs are re-prioritized the schedules so that prioritized jobs can get theavailable system resources. Furthermore, the characterization, analysis, and visualization of the reasons for a manual change in the schedule are to be considered. This work requires extensive data preprocessing and building machine learning models for the causal relationship between various system variables and the time of manual changes.
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