ERP Performance Optimization With Metadata Scoring for Lower Carbon Output
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Solution Overview
Problem
The increasing energy consumption and carbon emissions from IT systems, particularly in Enterprise Resource Planning (ERP) solutions, are not adequately addressed by existing technologies, and users are unaware of the environmental impact of program execution.
Innovation Solution
A system comprising a data extractor, data lake, and data analyzer is employed to scan multiple levels of an ERP solution, extract metadata, determine a standard score based on weight, severity, and violation count, and optimize performance using a learning model to reduce carbon output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If IT systems implement new solutions and increase computation power, then system functionality and performance improve, but energy consumption and carbon emissions increase
Solution Approach 1:
The system implements a feedback mechanism by continuously monitoring carbon emission levels during program execution and providing real-time feedback to users. The carbon emission monitoring module tracks emissions generated by specific programs, and this information is fed back to help users make informed decisions about program execution, thereby optimizing energy consumption while maintaining productivity.
Solution Approach 2:
The system enables self-service through automated carbon emission tracking and reporting. The monitoring module automatically captures execution data, calculates carbon emissions, and generates reports without requiring manual intervention. This self-service approach allows the system to optimize its own energy consumption patterns while providing users with actionable insights.
2Adaptability or versatility
If users increase dependence on IT programs and computer instructions, then application capabilities improve, but carbon emissions and environmental impact increase
Solution Approach 1:
The system introduces an intermediary layer between users and IT programs through the carbon emission monitoring module. This intermediary automatically tracks and reports carbon emissions generated by program execution, serving as a mediator that provides environmental impact information without interfering with the functionality or versatility of the underlying applications.
3Loss of information
If the system monitors and tracks carbon emission levels during program execution, then environmental awareness improves, but system complexity increases
Solution Approach 1:
The system segments the carbon emission monitoring functionality into a separate, modular component. The carbon emission monitoring module operates as an independent entity that can be integrated with existing IT systems without requiring fundamental changes to the core system architecture. This segmentation reduces overall system complexity while maintaining comprehensive environmental awareness.
4Measurement precision
If the system provides detailed insight information about rule violations, then optimization precision improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant carbon emission data and rule violation information for analysis and reporting. Rather than processing all possible data, the monitoring module selectively captures execution data, carbon emission levels, and rule violations that are most pertinent to optimization goals. This extraction approach maintains high optimization precision while reducing overall data processing requirements.
Data Source
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AI summary
A system for providing performance optimization for a software solution may scan multiple predefined levels of the software solution to extract corresponding metadata information from each of the multiple predefined levels. The system may store the extracted corresponding metadata information pertaining to standard parameters associated with performance of the software solution. The system may determine a standard score based on a plurality of attributes of the extracted corresponding metadata information, optimize the determined standard score based on training data received from a learning model, and generate an insight information comprising information related to determined rule violations and of evaluation steps involved in determining the determined standard score.