Control Parameter Optimization Using Bootstrapped Demand Simulation
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Solution Overview
Problem
Automated technology platforms face challenges in optimizing control parameters for resource management, balancing competing requirements like cost and availability, which is often impractical with manual ad hoc methods due to the complexity and scale of demand patterns.
Innovation Solution
A system and method that uses an advanced bootstrap process to generate statistically equivalent demand scenarios, coupled with a performance prediction process via Monte Carlo simulations, to automatically explore and identify optimal control parameter values that minimize operating costs while ensuring resource availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual ad hoc methods are used to optimize control parameters, then flexibility in adjusting parameters is maintained, but the complexity and scale of demand patterns make the optimization process impractical and time-consuming
Solution Approach 1:
The system performs self-service optimization by automatically analyzing demand patterns and adjusting control parameters without human intervention. The automated process collects demand data, identifies patterns, and optimizes parameters independently, eliminating the time-consuming manual optimization process while maintaining operational flexibility.
Solution Approach 2:
The patent replaces manual mechanical optimization processes with automated computational systems. Instead of human operators manually adjusting parameters based on experience, the system uses algorithms and data analysis to automatically optimize control parameters, significantly reducing the time required while handling complex demand patterns.
2Productivity
If automated optimization processes are implemented to handle complex demand patterns, then optimization efficiency and accuracy are improved, but the system complexity increases
Solution Approach 1:
The optimization system is segmented into distinct functional modules: demand data collection, pattern recognition, parameter optimization, and implementation. Each module handles a specific aspect of the optimization process, making the overall complex system manageable through modular design while maintaining high optimization efficiency.
Solution Approach 2:
The automated optimization system is designed to handle multiple types of demand patterns and optimize various control parameters across different resources using a unified framework. This multi-functional approach improves productivity by applying the same efficient optimization engine to diverse scenarios without proportionally increasing system complexity.
3Loss of energy
If control parameters are optimized to minimize operating costs, then cost efficiency is improved, but the ability to guarantee resource availability may be compromised
Solution Approach 1:
The system optimizes control parameters such as provisioning thresholds and scaling factors to minimize operating costs while maintaining resource availability. By carefully adjusting these parameters based on analyzed demand patterns, the system achieves cost efficiency without compromising the reliability and availability of resources, as the optimization considers both cost and performance metrics simultaneously.
Data Source
AI summary
A system and method are presented for optimizing choices of control parameters. A method includes collecting demand sequences each associated with a resource managed by a technology platform; processing a demand sequence for a resource to calculate an optimized control parameter (CP) value set to manage an automated process within the technology platform, wherein calculating includes: processing the demand sequence with an advanced bootstrap process to generate a collection of bootstrapped demand sequences; processing the bootstrapped demand sequences with a performance prediction process that models the automated process to predict a performance metric for an initially selected CP value set; identifying a neighborhood of CP value sets that includes neighbors and the initially selected CP value set; predicting the performance metric for each neighbor with the performance prediction process; and identifying from the neighborhood of CP value sets the optimized CP value set that provides a best performance metric.