Unified ML Model for IT Resource and KPI Forecasting
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Solution Overview
Problem
Current computing systems lack an automated mechanism to understand and forecast the impact of IT operations and issues on organizational performance key performance indicators (KPIs), leading to a disconnect between IT incident handling and organizational performance concerns, and there are no tools that can reliably reason over IT monitoring metrics and KPI measurements to provide forecasting of IT resource status impacts on organizational performance.
Innovation Solution
An improved computing tool that uses machine learning to train models based on historical data, generating a unified model of organizational processes and IT resources, which predicts IT resource impacts on KPIs and vice versa, and automatically generates remedial action recommendations with resource allocations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate monitoring systems are used for IT resources and organizational performance, then monitoring coverage is comprehensive, but system complexity increases and integration difficulty arises
Solution Approach 1:
The patent combines separate IT resource monitoring and organizational performance monitoring into a unified system that uses a single machine learning model to analyze both IT metrics and KPIs simultaneously. This integration reduces system complexity while maintaining comprehensive monitoring coverage by processing multiple data types through a common analytical framework.
Solution Approach 2:
The machine learning model serves multiple functions by simultaneously monitoring IT resource status, predicting KPI impacts, and generating remedial recommendations. This multi-functional approach eliminates the need for separate specialized systems while maintaining comprehensive monitoring capabilities across both IT and organizational performance domains.
2Ease of operation
If reactive incident handling is used, then response to known issues is straightforward, but organizational performance impact cannot be forecasted
Solution Approach 1:
The system performs preliminary forecasting of KPI impacts before incidents actually affect organizational performance. By analyzing current IT resource status and predicted future states, the system anticipates potential performance degradation and enables proactive remediation, transforming reactive incident handling into predictive performance management.
Solution Approach 2:
The system establishes feedback loops where predicted KPI impacts are continuously monitored and used to adjust remedial actions. This feedback mechanism ensures that incident handling remains simple while simultaneously providing comprehensive performance impact information, as the system automatically adjusts responses based on actual versus predicted outcomes.
3Adaptability or versatility
If manual resource allocation is used, then flexibility in decision-making is maintained, but resource optimization efficiency decreases
Solution Approach 1:
The system enables self-service resource allocation by automatically generating optimized remedial recommendations based on predicted KPI impacts. The machine learning model autonomously analyzes complex relationships between IT resources and organizational performance, providing actionable recommendations that maintain human decision flexibility while significantly improving resource allocation efficiency through data-driven optimization.
4Measurement precision
If comprehensive historical data is collected for analysis, then forecasting accuracy improves, but data processing complexity increases
Solution Approach 1:
The machine learning model acts as an intermediary that simplifies the processing of comprehensive historical data. Instead of requiring complex manual analysis of multiple data sources, the ML model automatically ingests diverse historical data including IT metrics, KPIs, and contextual information, transforming this complexity into accurate forecasting outputs through automated pattern recognition and predictive analytics.
Data Source
AI summary
Mechanisms are provided for forecasting information technology (IT) and environmental impacts on key performance indicators (KPIs). Machine learning (ML) computer model(s) are trained on historical data representing events and KPIs of organizational processes (OPs). The ML computer model(s) forecast KPI impact given events. Correlation graph data structure(s) are generated that map at least one of events to IT computing resources, or KPI impacts to OPs. A unified model is trained to model OPs and IT resources. The trained ML computer model(s) and unified model process input data to generate a forecast output that specifies at least one of a forecasted IT event or a KPI impact. The forecasted output is correlated with at least one of IT computing resource(s) or OP(s), at least by applying the correlation graph data structure(s) to the forecast output to generate a correlation output. A remedial action recommendation that comprises a resource allocation is generated based on the forecast output and correlation output.