IT Service Performance Tuning via Queuing Theory Models
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Solution Overview
Problem
Traditional performance tuning of IT services is a tedious and time-consuming iterative process that relies heavily on trial and error, as it involves analyzing individual nodes of IT services rather than treating the entire system holistically, making it inefficient for predicting the impact of changes on throughput and response time.
Innovation Solution
The use of queuing theory to model IT service performance, allowing for the prediction of changes by correlating system parameters with variables in a mathematical model, and creating performance maps to compare deviations from predefined norms, facilitating intra-industry comparisons through a database of performance maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional iterative performance tuning is used to analyze individual nodes, then detailed node-level optimization is achieved, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent segments the IT service into distinct nodes (web server, application server, backend server, database) and creates separate performance models for each node. This allows detailed analysis of individual node performance while using composite models to predict overall system behavior, reducing the need for exhaustive trial-and-error testing of the entire system.
Solution Approach 2:
The patent performs preliminary actions by creating mathematical models of each node's performance characteristics before actual performance tuning begins. These pre-established models allow prediction of system behavior under different configurations, eliminating the need for time-consuming iterative testing and enabling direct calculation of optimal settings.
2Measurement precision
If holistic modeling of the entire IT service is used, then prediction accuracy improves, but model complexity increases
Solution Approach 1:
The patent divides the complex holistic system into separable node-level models that can be independently developed and validated. Each node model captures local performance characteristics, and the composite model combines these simpler components to achieve accurate system-wide predictions without requiring an overly complex monolithic model.
Solution Approach 2:
The patent merges individual node performance models into a composite system model that predicts overall IT service performance. This combination allows the system to leverage simple, accurate node-level models to achieve holistic prediction accuracy, balancing model complexity with predictive capability.
3Ease of manufacture
If individual node analysis is performed, then specific node optimization is achieved, but system-wide performance prediction becomes difficult
Solution Approach 1:
The patent creates preliminary performance models for each node that encode the relationship between node configuration and performance output. These pre-established models allow simple node-level configuration changes to be automatically translated into system-wide performance predictions, preserving system-wide information while maintaining node-level simplicity.
Solution Approach 2:
The patent implements feedback mechanisms where node-level performance measurements are fed into the composite model to update system-wide performance predictions. This feedback loop ensures that simple node configurations are continuously correlated with accurate system-wide performance outcomes, preventing loss of system-level information.
Data Source
AI summary
Methods and systems are disclosed for modeling the performance of an IT service. The methods and systems take a holistic approach by treating the entire IT service instead of the individual nodes. In one implementation, the methods and systems provide a tool for mapping the performance of the IT service based on throughput and response time data. The tool may then be used to develop a mathematical model for the performance of the IT service. System parameters that may impact the performance of the IT service may be correlated to variables in the model. The model may then be used to predict the impact changes may have on the performance of the IT service. Performance maps for the same IT service may be compared over time to discern any departure from a norm. A database of performance maps may be established for a particular industry to facilitate intra-industry comparisons.