Per-Object Cost Estimation for Data Processing Sizing
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Solution Overview
Problem
Existing data processing system sizing methodologies fail to provide cost estimates on a per object or per transaction basis, making it difficult to assess the economic viability of hardware investments and performance goals.
Innovation Solution
A method that determines hardware requirements for each object of a selected application program, using a linear sizing model and cost estimation based on CPU, memory, and storage requirements, allowing for a total cost estimate broken down by individual objects, enabling a plausibility check and efficient configuration options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sizing methodologies are used, then hardware requirements can be determined, but cost estimates on a per object or per transaction basis cannot be provided
Solution Approach 1:
The patent segments the hardware requirements and costs by individual objects or transactions within the application program. Instead of providing a single aggregate hardware size, the system breaks down CPU, memory, and storage requirements for each object, enabling precise cost estimation per object while maintaining manageable complexity through automated calculation processes.
2Loss of information
If detailed object-specific hardware requirements are calculated, then cost estimates can be broken down by individual objects, but the calculation process becomes more complex
Solution Approach 1:
The system performs preliminary calculations by establishing object-specific hardware requirements before final cost estimation. The linear sizing model pre-calculates CPU, memory, and storage needs for each object based on application parameters, enabling detailed cost breakdowns without requiring complex real-time calculations during the estimation process.
3Reliability
If per object cost estimates are provided, then ROI can be assessed, but existing sizing tools do not support this level of detail
Solution Approach 1:
The patent changes the parameter structure from aggregate hardware sizing to object-specific parameter estimation. By introducing object-level parameters for CPU requirements, memory requirements, and storage requirements, the system enables reliable ROI assessment while adapting existing sizing methodologies to provide granular cost information that supports financial decision-making.
Data Source
AI summary
Systems and methods for providing a cost estimate for a data processing system are provided. An exemplary method may include selecting application programs from a set of application programs, wherein each application program may have a number of objects. The method may further include entering data descriptive of a load profile, retrieving a set of sizing coefficients for each object of the selected application programs, and estimating the hardware requirements for each one of the objects of the selected application programs by entering the sizing coefficients and the load profile into a sizing model. The method may further include entering the hardware requirements for each one of the objects of the selected application programs into a cost estimation component to provide a cost estimate for each one of the program objects of the selected application programs. The method may further include calculating a total hardware requirement by adding the hardware requirements, and calculating the total cost estimate for the data processing system by adding the cost estimates for each object of the selected application programs.