Capacity Model Run-Time Estimator for Transactional Capacity
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Solution Overview
Problem
Current methods for determining transactional capacity of applications are informal and rely on experience, lacking a formalized approach, leading to inaccurate assessments and insufficient insight for investment decisions, resulting in financial and reputational impacts.
Innovation Solution
A capacity model run-time estimator that uses historical data, regression analysis, and optimization solvers to determine transactional capacity by considering transaction characteristics, operational processes, and constraints across various times, enabling accurate forecasting and planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If experience-based capacity analysis is used, then the assessment process is simple and quick, but the accuracy and reliability of capacity determination deteriorates
Solution Approach 1:
The patent replaces the mechanical/manual experience-based capacity analysis with an automated computerized system that collects performance data, executes capacity analysis procedures, and generates capacity determinations automatically, thereby maintaining simplicity and speed while dramatically improving accuracy through systematic data-driven analysis
Solution Approach 2:
The system enables self-service capacity analysis by automatically collecting performance data from multiple sources, executing the capacity analysis procedure without human intervention, and generating capacity determinations that can be directly used for investment decisions, eliminating the need for expert judgment while maintaining operational simplicity
2Measurement precision
If a formalized capacity analysis procedure is implemented, then the accuracy and insight for investment decisions is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the capacity analysis into distinct components: data collection from multiple performance sources, execution of the capacity analysis procedure, and generation of capacity determinations. This modular approach allows each component to be optimized independently while maintaining overall system manageability and reduced complexity
Solution Approach 2:
The computerized system acts as an intermediary between raw performance data and capacity determination, automatically processing and analyzing the data through the formalized procedure. This intermediary layer simplifies the interface for users while implementing the complex analysis procedures in the background
3Ease of operation
If single-dimensional transaction volume analysis is used, then the analysis process is simple, but the insight and transparency for capacity planning deteriorates
Solution Approach 1:
The patent transitions from single-dimensional transaction volume analysis to multi-dimensional analysis by incorporating multiple performance data sources and analyzing multiple capacity dimensions simultaneously, providing comprehensive insight while maintaining ease of use through automated processing and clear presentation of results
Data Source
AI summary
Embodiments of the present invention provide apparatuses and methods for a capacity model run-time estimator that determines the transactional capacity of an application. The capacity model run-time estimator is based on a model that includes at least one job with various steps within a run-time window. One or more windows make up shifts and one or more shifts make up a day. Historical data is used to estimate various run-times for each of the steps in a window. If the steps are dependent on transaction characteristics a statistical analysis is used to determine a run-time window estimator model that is dependent on the transaction characteristics. Constraints are placed on the run-time and transaction capacity of the windows, intra-windows, and shifts. The capacity model uses the run-time window estimators and constraints to determine the transaction capacity for an application across various periods and compares the results to forecasted capacity.