Attribute-Based Framework for Root Cause Determination

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Solution Overview

Problem

Current system management frameworks lack a simplistic and effective method for problem detection and determination in computer storage systems, particularly in identifying performance abnormalities and their root causes, which is crucial for proactive management and goal alignment.

Innovation Solution

A method that monitors system state, workload, and performance parameters, compares them against normal behavior using a workload-to-performance mapping, summarizes abnormalities as computation and data-processing attributes, and communicates the root cause using an attribute-based framework, enabling query-style interaction between problem determination and corrective action engines.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If conventional event-based problem determination frameworks are used, then the interface between CAE and PD is established, but the complexity of making sense of collected information and determining root causes increases significantly

Engineering Contradiction:

Improveease of problem determinationVSAvoidcomplexity of information analysis

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent segments the problem determination process into distinct components: data collection, data analysis, and root cause determination. It introduces a layered architecture where each layer (storage layer, controller layer, host layer) independently analyzes performance abnormalities, making the overall complex task manageable through modular segmentation.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary attribute-based framework that mediates between raw monitored data and final root cause determination. This framework translates complex performance abnormalities into standardized attributes (computation attributes, data-processing attributes, I/O attributes), simplifying the analysis process and enabling systematic root cause identification.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If domain-specific information models are used for problem determination, then the analysis can be tailored to specific systems, but the complexity and domain-dependence of the solution increases

Engineering Contradiction:

Improveaccuracy of problem determinationVSAvoidcomplexity of domain-specific models

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent creates a universal problem determination framework that can be applied across different storage systems (SAN, NAS, storage area networks) without requiring domain-specific models. The attribute-based framework and layered architecture provide a general-purpose approach that adapts to various storage configurations, eliminating the need for complex domain-specific information models while maintaining high reliability through systematic analysis.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Reliability

If proactive interpretation of abnormal system behavior is implemented, then corrective actions can be taken before violations occur, but the complexity of predicting and preventing problems increases

Engineering Contradiction:

Improveproactive system managementVSAvoidcomplexity of behavior prediction

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent enables proactive management by continuously monitoring system parameters and comparing them against baseline performance characteristics. When deviations are detected that indicate emerging problems, the system can trigger preventive corrective actions before actual goal violations occur. This is achieved through the layered analysis framework that identifies performance abnormalities early in their development.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms where monitored performance data is continuously fed back into the analysis framework. The system compares current performance against historical baselines and adjusts its predictions and corrective actions accordingly. This feedback loop enables adaptive proactive management that learns from past behavior patterns to better predict and prevent future problems.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS7673189B2Technique for mapping goal violations to anamolies within a system

Publication Date: 2010.03.02 X CORP

AI summary

A computer and method for problem detection and determination for automated system management in a system, wherein the method comprises monitoring system state, workload, and performance parameters of the system; comparing the monitored parameters against normal system performance behavior of the system, wherein the normal system performance behavior is maintained as a mapping of a system state and workload-to-performance parameters; summarizing performance abnormalities at a specified layer in the system as computation and data-processing attributes, wherein the performance abnormalities comprise deviations from the normal system performance behavior; correlating the performance abnormalities across multiple layers in the system using an attribute-based framework; and communicating a root-cause of the performance abnormalities.