SAN Problem Diagnosis via Resource Map Responsibility Factors
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Solution Overview
Problem
Complex storage-area-network (SAN) environments are difficult to diagnose due to their complexity and the need for administrators to analyze large logs and correlate failures across numerous components, often requiring specialized experience.
Innovation Solution
A method and system that generate a resource map identifying components and their importance, calculate potential cause and problem responsibility factors, and prioritize components based on these factors to quickly identify the root cause of issues in SANs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If administrators manually analyze large numbers of logs and correlate failures across components, then diagnostic accuracy may be improved, but the time required and complexity of troubleshooting increases significantly
Solution Approach 1:
The patent introduces an intermediary system that automatically collects, correlates, and analyzes alerts from multiple SAN components. This intermediary processing layer translates raw alert data into structured diagnostic information, eliminating the need for administrators to manually analyze logs while maintaining high diagnostic accuracy through systematic correlation of failures across components.
Solution Approach 2:
The patent replaces the manual mechanical process of log analysis with an automated electronic system. The system automatically gathers alerts, synchronizes timestamps, correlates failures, and presents diagnostic results, substituting human administrative effort with automated computing processes that operate faster and without fatigue.
2Adaptability or versatility
If the storage network includes hundreds or thousands of components coupled together, then storage capacity and functionality are improved, but the complexity of understanding and troubleshooting the network increases
Solution Approach 1:
The patent segments the complex SAN troubleshooting process into distinct analytical steps: alert collection, timestamp synchronization, failure correlation, and diagnostic presentation. By breaking down the overwhelming complexity into manageable segments, the system makes the diagnostic process understandable and tractable even for networks with hundreds or thousands of components.
Solution Approach 2:
The patent introduces an intermediary diagnostic system that acts as a mediator between the complex SAN infrastructure and administrators. This intermediary automatically performs the complex task of correlating failures across numerous components, presenting simplified diagnostic information that reduces the perceived complexity while maintaining full awareness of the underlying complex interactions.
3Measurement precision
If administrators require specialized experience to diagnose SAN problems, then diagnostic accuracy is improved, but the ease of operation for general administrators deteriorates
Solution Approach 1:
The patent implements a self-service diagnostic system that automatically performs the expert-level analysis previously requiring specialized administrator experience. The system autonomously collects alerts, synchronizes timestamps, correlates failures across components, and presents diagnostic conclusions, enabling any administrator to achieve expert-level diagnostic accuracy without requiring specialized training or experience.
Data Source
AI summary
A method to more effectively and efficiently diagnose problems in a storage network is disclosed. In one embodiment, such a method includes generating, for a storage network, a resource map that identifies components therein. For each component in the resource map, the method identifies an importance factor that indicates the component's importance in the storage network. When a problem is detected in the storage network, the method identifies, for each component in the resource map, a potential cause factor that indicates how likely the component is to have caused the problem. The method also calculates, for each component in the resource map, a problem responsibility factor to indicate to what extent the component is held responsible for the problem. A corresponding system and computer program product are also disclosed.