Automated Configuration Rule Inference for Complex Systems
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Solution Overview
Problem
Current methods for managing complex computer systems are inefficient in inferring and documenting configuration rules and version management, often relying on interviews and incomplete records, which can lead to loss of critical information due to personnel changes or system evolution.
Innovation Solution
An automated system using simulated annealing and genetic programming to infer logical rules from configuration data, optimizing for simplicity and accuracy in predicting current and historical system configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual interviews and incomplete records are used to infer configuration rules, then personnel changes and system evolution cause information loss, but automated systems increase complexity and resource requirements
Solution Approach 1:
The system performs self-service by automatically inferring configuration rules from existing configuration data without requiring manual interviews or external input. The genetic programming algorithm autonomously analyzes configuration datasets and generates rules, eliminating dependence on human personnel who may leave or change roles.
Solution Approach 2:
The patent replaces the mechanical process of manual interviews and record-keeping with an automated computational system. Instead of relying on human administrators to recall or document rules, the system uses genetic programming algorithms to automatically discover and infer configuration rules from historical configuration data.
2Reliability
If automated systems are implemented to infer configuration rules, then information recovery improves, but computational resources and processing time increase
Solution Approach 1:
The system applies partial action by using genetic programming to evolve a subset of relevant configuration rules from the data, rather than attempting to analyze every possible configuration parameter. The algorithm focuses computational effort on discovering the most significant rules that explain configuration patterns, avoiding unnecessary processing of irrelevant data.
Solution Approach 2:
The system changes parameters by adjusting the fitness function and evaluation criteria during the genetic programming process. By modifying how candidate rules are scored and selected, the system optimizes the balance between rule accuracy and computational efficiency, finding configurations that provide reliable results with reasonable resource consumption.
3Manufacturing precision
If comprehensive configuration tracking is implemented, then system administration accuracy improves, but system complexity and maintenance burden increase
Solution Approach 1:
The system extracts only the essential configuration rules from the comprehensive configuration data, separating the critical information needed for accurate system administration from the vast amount of raw configuration data. This extraction process produces a concise set of inferred rules that maintain accuracy without requiring management of the entire configuration dataset.
Solution Approach 2:
Instead of starting with comprehensive configuration management procedures and trying to maintain them, the system inverts the approach by first analyzing configuration data to automatically infer the rules. This reversal simplifies the process by deriving management guidelines from actual system behavior rather than imposing complex predetermined structures.
Data Source
AI summary
The current document discloses an automated method and system for inferring the logical rules underlying the configuration and versioning state of the components and subcomponents of a complex system, including data centers and other complex computational environments. The methods and systems employ a database of configuration information and construct an initial set of logical rules, or hypotheses, regarding system configuration. Then, using simulated annealing and a variant of genetic programming, the methods and systems disclosed in the current document carry out a search through the hypothesis state space for the system under several constrains in order to find one or more hypotheses that best explain the configuration and, when available, configuration history. The constraints include minimization of the complexity of the hypotheses and maximizing the accuracy by which the hypotheses predict observed configuration and configuration history.