Management Computer Causality Analysis Storage Optimization
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Solution Overview
Problem
Existing management computer systems require significant storage resources to create and maintain causality matrices for analyzing complex computer systems with large-scale or numerous error propagation models, leading to inefficiencies in storage usage.
Innovation Solution
Implement a management computer system that stores topologies, event propagation models, and causality information, allowing for on-demand creation and expansion of causal relations based on detected events, reducing the need for pre-computed causality matrices and optimizing storage usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a causality matrix is created based on all managed apparatuses and all error propagation models before starting event analysis, then complete causality analysis is achieved, but storage resources (memory and secondary storage) are significantly consumed
Solution Approach 1:
The patent divides the causality matrix into multiple sub-matrices, each corresponding to a specific error propagation model. Instead of creating one large matrix containing all possible causal relationships, the system creates smaller sub-matrices on-demand for each model, reducing overall storage requirements while maintaining analysis completeness.
Solution Approach 2:
The patent pre-creates only the essential components (topology information and error propagation models) before event analysis, rather than pre-computing the entire causality matrix. The actual causality relationships are generated dynamically when needed, reducing initial storage consumption while ensuring complete analysis capability.
2Adaptability or versatility
If the causality matrix is expanded to handle elaborate computer systems with large-scale error propagation models, then analysis capability is improved, but storage resource usage increases significantly
Solution Approach 1:
The patent implements a dynamic causality matrix structure that can be expanded and contracted based on the specific analysis needs. The matrix is not fixed in size but adapts dynamically by loading only the necessary sub-matrices for the current error propagation model being analyzed, allowing the system to handle elaborate computer systems without proportional increases in storage usage.
Solution Approach 2:
The patent applies different storage strategies to different parts of the causality analysis system. Frequently accessed or essential sub-matrices are kept in memory, while less frequently accessed sub-matrices are stored in secondary storage. This local optimization allows the system to handle large-scale error propagation models while minimizing overall storage resource usage.
Data Source
AI summary
In analyzing an elaborate computer system which requires large-scale or numerous event propagation models, a law-of-causality matrix gains size, so that significant amount of storage resources has been used in a management computer. To solve such a problem, the management computer to manage the computer system stores topologies, event propagation models, and causality information including one or more causal relations in the storage resources, determines, when the management computer analyzes or detects an event, whether a causal relation has already been created for the event to be analyzed, and creates the causal relation based on a topology and event propagation models, if not yet.