SaaS Code Complexity Metering via Computational Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud metering mechanisms for Software as a Service (SaaS) applications on Platform as a Service (PaaS) servers are not comprehensive, primarily focusing on resource utilization metrics like API calls, users, and infrastructure usage, failing to account for application code complexity, which is essential for accurate pricing and resource optimization.
Innovation Solution
A computer-implemented method that analyzes the source code of SaaS applications to generate computational vectors based on extracted parameters, applying vector policies and scoring algorithms to calculate a complexity score, which is then used to determine a metering metric for charging and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current metering mechanisms focus only on resource utilization metrics, then infrastructure monitoring is simplified, but measurement precision is insufficient for accurate pricing
Solution Approach 1:
The patent segments the metering mechanism into multiple independent components: code complexity analyzer, resource utilization tracker, computational vector generator, and pricing calculator. Each component handles a specific aspect of metering, allowing the system to achieve comprehensive measurement precision while maintaining modularity and manageable complexity in each individual component.
Solution Approach 2:
The patent adds a new dimension to traditional metering by incorporating code complexity analysis alongside resource utilization metrics. This multi-dimensional approach transforms the metering system from single-metric tracking to comprehensive multi-parameter measurement, enabling precise pricing that reflects both infrastructure consumption and application complexity.
2Measurement precision
If runtime execution is required for metering, then accurate resource usage data is obtained, but performance and cost-effectiveness decrease
Solution Approach 1:
The patent performs code complexity analysis and parameter extraction during application deployment or idle periods rather than during runtime execution. Computational vectors are pre-calculated and stored, enabling the metering system to retrieve pre-computed data during operation without impacting application performance or requiring additional runtime computational resources.
3Measurement precision
If comprehensive metering metrics are implemented, then pricing accuracy improves, but device complexity increases
Solution Approach 1:
The patent creates a universal metering framework that handles multiple types of metrics (code complexity, resource utilization, computational vectors) through a single integrated system. The computational vector generation mechanism serves multiple functions: analyzing code complexity, extracting parameters, and preparing data for pricing calculations, thereby reducing overall system complexity through functional consolidation.
Data Source
AI summary
Metering based on application code complexity is provided. Code of an application to be executed is received from a user. Values of parameters corresponding to the application are extracted based on analysis of the code. A set of computational vectors corresponding to extracted values of the parameters of the application is generated to calculate a complexity of the code. A metering metric to charge the user to execute the application is determined based on the complexity of the code. The application is executed at the metering metric based on the complexity of the code.