Sequential File Size Correlation for Mainframe Resource Allocation
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Solution Overview
Problem
In mainframe computing environments, the size of output files produced by customer programs is conventionally unknown before execution, leading to potential failures and decreased performance due to insufficient resource allocation.
Innovation Solution
A system that predicts output file sizes and resource consumption using historical data analysis, employing linear regression and neural networks to correlate input file sizes with output file sizes and resource requirements, allowing for optimal memory allocation and job scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined output file sizes are allocated for sequential files, then memory allocation is simplified, but the allocation may not be optimal leading to potential failures and decreased performance
Solution Approach 1:
The system performs preliminary analysis of historical job execution data to predict output file sizes before actual program execution. This advance preparation allows optimal memory allocation to be predetermined, preventing failures due to insufficient space while avoiding the complexity of dynamic adjustment during execution.
Solution Approach 2:
The system establishes feedback loops that continuously collect actual output file sizes from executed jobs and use this information to refine prediction models. This feedback mechanism improves prediction accuracy over time, enabling more reliable resource allocation without increasing operational complexity.
2Quantity of substance
If conventional predefined sizes are used for output files, then resource allocation is straightforward, but it is not known how much output file space is required leading to insufficient allocation
Solution Approach 1:
The system calculates predicted output file sizes in advance by analyzing historical data patterns before job execution. This preliminary determination of required space eliminates the need for time-consuming adjustments during execution and ensures sufficient allocation from the start.
Solution Approach 2:
The system automatically determines output file space requirements through self-learning from historical execution data. This self-service capability eliminates manual intervention for resource allocation while providing accurate predictions, saving time without sacrificing allocation adequacy.
3Productivity
If output file sizes are predicted using historical data analysis, then optimal memory allocation is achieved, but system complexity increases due to prediction mechanisms
Solution Approach 1:
The prediction system operates autonomously by automatically collecting historical execution data, analyzing patterns, and generating predictions without requiring manual configuration or intervention. This self-service approach improves productivity through accurate resource allocation while minimizing the operational complexity burden on users.
Solution Approach 2:
The system implements feedback mechanisms where actual execution results are continuously fed back to refine prediction models. This automated feedback loop enhances prediction accuracy and resource utilization efficiency while the system self-adjusts, avoiding the need for complex manual tuning and maintenance.
Data Source
AI summary
A system for use in predicting resources required for a program. The system including a processor, a storage device accessible by the processor, and a sequential file prediction program that when executed by the processor configures the system to access a history file to determine sizes of past sequential input files input to a customer program and sizes of resultant past sequential output files produced by the customer program processing the sequential input files, determine a correlation between the sizes of the past sequential input files and the resultant sizes of the past sequential output files, utilize the correlation to predict future sizes of future sequential output files based on the current sizes of current sequential input files, and utilize the predicted future consumption of the scalable resources to perform at least one of memory allocation or to determine scheduling for batch jobs being performed by the system.