Fuzzy Cause Effect Engine for IT Infrastructure Visibility
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Solution Overview
Problem
Current information technology infrastructure management systems lack the ability to effectively combine and analyze potential causes and effects within cloud and virtualized environments, leading to inadequate troubleshooting and visibility, resulting in prolonged issue resolution times and increased downtime.
Innovation Solution
A system and method that determine fuzzy cause and effect relationships by integrating various input sources, using a model-driven and service-oriented architecture to aggregate and analyze data from user identities, access credentials, physical and virtual resources, and services, employing a fuzzy cause and effect engine to combine potential causes and effects with instantaneous feedback mechanisms, and generating visual diagrams to represent relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional monitoring systems are used to track IT infrastructure, then basic visibility is provided, but complex cause and effect relationships cannot be identified
Solution Approach 1:
The patent introduces a fuzzy cause and effect engine as an intermediary component that sits between data collection mechanisms and analysis tools. This engine processes raw monitoring data and generates fuzzy logic rules that represent causal relationships, thereby mediating between simple monitoring and complex analysis requirements
Solution Approach 2:
The patent replaces traditional mechanical correlation methods with fuzzy logic-based causal inference. Instead of using deterministic rules or simple statistical correlation, the system employs fuzzy logic engines that can handle uncertainty and partial truths in causal relationships
2Loss of information
If comprehensive data collection from all sources is implemented, then complete visibility is achieved, but processing time and system complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-computing fuzzy causal relationships and storing them in a knowledge base before actual troubleshooting occurs. When issues arise, the system queries pre-established causal rules rather than computing relationships in real-time, significantly reducing resolution time
Solution Approach 2:
The patent segments the data collection and analysis process into distinct modules: data collection from multiple sources, fuzzy logic rule generation, causal relationship inference, and troubleshooting recommendation. This segmentation allows each component to be optimized independently and processed in parallel
3Measurement precision
If fuzzy logic analysis is applied to determine cause and effect relationships, then accurate troubleshooting is achieved, but computational requirements increase
Solution Approach 1:
The patent applies parameter changes by adjusting the complexity and granularity of fuzzy logic rules based on the specific troubleshooting context. The system dynamically modifies parameters such as rule depth, data sampling rate, and inference granularity to balance precision requirements with computational energy consumption
Data Source
AI summary
The system and method for determining fuzzy cause and effect relationships in an intelligent workload management system described herein may combine potential causes and effects captured from various different sources associated with an information technology infrastructure with substantially instantaneous feedback mechanisms and other knowledge sources. As such, fuzzy correlation logic may then be applied to the combined information to determine potential cause and effect relationships and thereby diagnose problems and otherwise manage interactions that occur in the infrastructure. For example, information describing potential causes and potential effects associated with an operational state of the infrastructure may be captured and combined, and any patterns among the information that describes the multiple potential causes and effects may then be identified. As such, fuzzy logic may the be applied to any such patterns to determine possible relationships among the potential causes and the potential effects associated with the infrastructure operational state.