Service Workload Prediction via Load-Performance Correlation
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Solution Overview
Problem
Current solutions for predicting service delivery workloads and infrastructure requirements fail to effectively address the impact of increased user request loads and changes in IT infrastructure, as they do not provide sufficient insights into the necessary efforts needed to remediate potential problems.
Innovation Solution
A method that collects and correlates data on load values, performance values, event outputs, and ticket volumes to predict new performance metrics, using queuing and regression analysis techniques to estimate ticket and event volumes based on projected system loads and configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If system performance data is collected and correlated to predict service delivery metrics, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the prediction process into distinct modules: data collection from multiple sources, data preprocessing and cleaning, correlation analysis between system performance metrics and service delivery outcomes, and prediction model generation. Each module handles specific tasks independently, improving prediction accuracy while managing complexity through functional decomposition.
Solution Approach 2:
The patent introduces intermediary components including data preprocessing layers that clean and standardize inputs before analysis, and correlation analysis mechanisms that serve as mediators between raw performance data and final predictions. These intermediaries simplify the overall processing complexity by handling data transformation and relationship analysis in dedicated stages.
2Reliability
If multiple data sources are integrated for comprehensive prediction, then prediction completeness is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal data collection framework that integrates multiple data sources including system performance metrics, service delivery data, and operational logs through a common interface. This multi-functional system handles diverse input types uniformly, ensuring prediction completeness while managing system complexity through standardized processing pathways.
Solution Approach 2:
The patent merges multiple data sources and processing functions into an integrated prediction system. By combining data collection, preprocessing, correlation analysis, and prediction generation into a unified workflow, the system achieves comprehensive prediction coverage while reducing overall complexity compared to separate independent systems.
Data Source
AI summary
A method for predicting a computerized service delivery organization workload including collecting data of a computer implementing service delivery routine including overlapping samples of load values, overlapping samples of performance values, overlapping samples of event outputs, ticket values and ticket volumes, building a first correlation of said load values with said performance values for predicting new performance values based on new data, building a second correlation of said performance values with said event outputs, said ticket values and said ticket volumes, combining said first and second correlations into a third correlation for correlating said load values with a ticket volume and an event volume, and determining at least one projected event volume or projected ticket volume value using said third correlation and at least one projected load value of said computer.