Transaction Parallelization Metric Calculation for Multi-Domain Systems
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Solution Overview
Problem
In complex multi-threaded environments, existing application performance management systems struggle to effectively monitor and improve transaction parallelization, leading to inefficient resource utilization and suboptimal CPU and communication channel usage.
Innovation Solution
A method and system for determining a transaction parallelization improvement metric by analyzing tracking data on inbound and outbound subtransactions between domains, calculating a parallelization metric, and using this data to assess the difficulty of improving parallelization efficiency, allowing for customized optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of domains is increased to improve parallelization, then resource utilization improves, but the complexity of monitoring and measuring transaction interactions increases
Solution Approach 1:
The patent introduces an intermediary metric calculation system that aggregates complex multi-domain transaction data into simplified parallelization metrics. This intermediary layer processes tracking data from multiple domains and presents consolidated performance measurements, reducing the monitoring complexity while maintaining accurate parallelization assessment across increased numbers of domains.
Solution Approach 2:
The patent transforms complex transaction interaction data into simplified parallelization improvement metrics by changing the measurement parameters. Instead of directly monitoring all individual transaction interactions across multiple domains, the system calculates derived metrics that represent parallelization efficiency, making measurement feasible even as the number of domains increases.
2Productivity
If transaction parallelization is improved by adding more domains, then resource utilization increases, but the device complexity increases
Solution Approach 1:
The patent segments the complex system into distinct functional components: tracking data collection from domains, interaction counting logic, domain identification, and metric calculation modules. This segmentation allows each component to handle specific aspects of parallelization measurement independently, reducing overall system complexity while enabling support for increased numbers of domains.
Solution Approach 2:
The patent creates a universal metric calculation framework that can handle any number of domains and transaction types through a single standardized approach. The parallelization improvement metric calculation uses consistent formulas and data structures that work across varying system complexities, allowing the same system architecture to scale from few to many domains without proportional increases in complexity.
3Measurement precision
If tracking data is collected from multiple domains to calculate parallelization metrics, then measurement precision improves, but the loss of time for data collection increases
Solution Approach 1:
The patent implements preliminary action by having domains pre-generate and send tracking data for each transaction interaction as transactions occur, rather than performing retrospective data collection. This preliminary data collection approach ensures accurate parallelization metrics while minimizing time loss, as the data is captured at the source during normal operation and immediately made available for metric calculation.
Data Source
AI summary
A method, program product, and system for a transaction parallelization improvement metric calculation includes receiving tracking data. The tracking data includes information about inbound and outbound subtransactions between domains over a time frame. Each domain includes at least one computer. Using the tracking data received, a number of interactions are determined based on a number of the inbound and outbound subtransactions. A total number of domains is determined using received tracking data. A transaction parallelization metric is calculated using the tracking data and a transaction parallelization improvement metric is calculated based on the number of interactions, the number domains, and the transaction parallelization metric.