Mainframe Job Error Healing via Metadata-Driven Resource Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mainframe job failures due to unavailability of resources such as data files, database tables, and missing file attributes lead to inefficient processing and delays, as existing systems lack effective predictive and corrective mechanisms to prevent and resolve these failures in a timely manner.
Innovation Solution
The implementation of a jobs manager system that checks metadata associated with mainframe jobs to identify required resources, places jobs on hold until resources are available, and applies corrective actions to re-process failed jobs based on error codes and corresponding actions stored in metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mainframe jobs are processed without pre-checking resource availability, then processing speed is maintained, but job failures occur due to unavailable resources
Solution Approach 1:
The system performs preliminary actions by checking resource availability (data files, database tables, file attributes) before initiating mainframe job processing. The jobs manager validates all required resources exist and are accessible, placing jobs on hold only when necessary resource unavailability is detected. This preventive approach ensures jobs start with all required resources available, eliminating runtime failures while minimizing delays through efficient pre-validation.
2Reliability
If mainframe jobs are held until resources are available, then job failures are prevented, but processing time increases
Solution Approach 1:
The system implements continuous feedback monitoring of resource availability status for jobs on hold. The jobs manager periodically checks whether required resources (data files, database tables, file attributes) have become available and automatically triggers job processing when resources are confirmed present. This feedback mechanism prevents unnecessary prolonged delays while ensuring jobs only start when all resources are guaranteed available, balancing reliability with processing efficiency.
3Productivity
If error corrective actions are automatically applied to failed jobs, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The system implements self-service by enabling the jobs manager to automatically detect job failures, identify corresponding corrective actions from stored metadata, and apply those corrections without human intervention. When a job fails, the system retrieves the error code, matches it to a predefined corrective action (such as recreating a data file or correcting a file attribute), and reprocesses the job automatically. This self-correcting capability significantly improves processing efficiency by eliminating manual error resolution while the structured metadata-based approach keeps system complexity manageable.
Data Source
AI summary
A system includes a memory and a processor configured to detect that a primary mainframe job has failed, and extracts information related to the failed primary mainframe job, wherein the extracted information includes an error description of an error that caused the primary mainframe job to fail. The processor searches the memory based on the error description for a set of corrective actions corresponding to the error. In response to determining the set of corrective actions based on the search, the processor obtains a criticality index relating to the primary mainframe job. When the criticality index is lower than a criticality index threshold, the processor applies the set of corrective actions in relation to the failed primary mainframe job and processes the failed primary mainframe job after applying the set of corrective actions.