Transaction Load Modeling for High-Volume Performance Evaluation
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Solution Overview
Problem
Current financial instrument trading systems face challenges in verifying and evaluating performance under heavy loads of actual production data, leading to potential errors and failures during high trading volumes.
Innovation Solution
A configurable and scalable transaction rate input model is used to simulate historical transaction input rates, applying multipliers to amplify load values and inject actual production data elements into the system to evaluate performance accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual production data is used to evaluate system performance under heavy loads, then measurement precision is improved, but system reliability deteriorates due to potential errors and failures during high trading volumes
Solution Approach 1:
The patent applies preliminary action by organizing and preparing production data elements in advance into a contiguous set before evaluation. The system sections the data into portions, determines data element counts for each portion, and prepares the data for amplified loading before actually subjecting the transaction processing system to heavy loads. This pre-preparation ensures that when high-volume testing occurs, the data is already structured and ready, preventing evaluation errors while maintaining system stability.
Solution Approach 2:
The patent uses copying by creating a controlled evaluation environment that replicates production conditions without using actual production data during the test. The system organizes production data elements into a contiguous set and sections them into portions for amplified loading, effectively creating a copy of production data structures that can be safely manipulated and amplified without risking actual production system integrity.
2Measurement precision
If transaction load is increased to simulate real-world conditions, then measurement precision is improved, but device complexity increases due to need for data organization and amplification mechanisms
Solution Approach 1:
The patent applies segmentation by dividing the contiguous set of production data elements into multiple portions. Each portion is assigned a specific number of data elements determined by sectioning the original set. This segmentation allows the system to manage large volumes of data in manageable chunks, applying amplification factors to each portion separately while maintaining overall measurement precision without overwhelming system complexity.
Solution Approach 2:
The patent implements dynamics by making the data organization structure adaptable and configurable. The system allows configurable amplification factors that can be adjusted based on evaluation needs, and the data portions can be dynamically assigned different amplification levels. This dynamic approach enables precise control over load intensity without requiring a completely rigid complex infrastructure.
3Productivity
If production data is organized and amplified for evaluation, then productivity is improved through controlled testing, but loss of time increases due to data preparation and organization processes
Solution Approach 1:
The patent applies preliminary action by organizing production data elements into a contiguous set and sectioning them into portions before the actual evaluation process. This pre-organization happens once, and the prepared data structure can then be reused for multiple evaluations with different amplification factors, significantly reducing the time needed for subsequent testing while maintaining high productivity.
Solution Approach 2:
The patent implements universality by creating a data organization structure that serves multiple evaluation purposes. The contiguous set of production data elements, once organized and sectioned, can be used for various types of performance evaluations with different amplification factors and configurations. This multi-functional data structure eliminates the need to reorganize data for each evaluation scenario, reducing time loss while maintaining evaluation productivity.
Data Source
AI summary
A transaction evaluation system may include a configurable and/or scalable transaction rate input model which models historical and/or recent transaction input rate patterns of a transaction processing system for a specified time period with configurable and/or scalable transactional amplitude for use in evaluating performance of the transaction processing system. The system may operate to inject particular volumes of transactions into the transaction processing system at specific times and/or periods of time.