Causal Variance Ranking for Root Cause Analysis in Distributed Computing
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Solution Overview
Problem
As computing environments become increasingly complex and distributed, managing application impairments and anomalies in multi-tiered computing ecosystems is challenging due to the complexity of identifying causal services and performing effective remediation.
Innovation Solution
A method for managing distributed multi-tiered computing environments involves obtaining correlated services associated with anomalies, generating a service dependency graph, calculating causal variance, and creating a weighted rank order of causal services to prioritize remediation efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to identify causal services in complex distributed computing environments, then analysis accuracy may be maintained, but the time required for root cause identification increases significantly
Solution Approach 1:
The patent introduces an intermediary system comprising a service dependency graph and causal variance calculation mechanism that mediates between service impairment detection and root cause identification. This intermediary layer automatically processes service correlations and dependencies, reducing the time required for manual analysis while maintaining identification accuracy through structured data representation and mathematical variance calculations.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational methods. Specifically, it substitutes human analysts examining service dependencies with algorithmic processing that calculates causal variance and generates ranked lists of causal services automatically, dramatically reducing analysis time while preserving accuracy through systematic computational approaches.
2Measurement precision
If comprehensive service dependency graphs are constructed to improve root cause analysis accuracy, then identification precision improves, but system complexity and computational resources required increase
Solution Approach 1:
The patent extracts only the essential and relevant service dependencies needed for causal analysis rather than incorporating all possible service relationships. By selectively extracting critical dependency information and focusing on services directly related to the anomaly, the system achieves high identification accuracy while avoiding the complexity of complete service graph representation.
Solution Approach 2:
The patent applies local quality by constructing service dependency graphs with varying levels of detail based on local requirements. Different domains or service clusters can have customized dependency representations tailored to their specific analytical needs, allowing high precision in critical areas while reducing overall system complexity through localized optimization rather than uniform comprehensive modeling.
3Productivity
If automated remediation systems are implemented to reduce response time, then productivity improves, but the risk of incorrect remediation actions increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing service dependency relationships, causal variance metrics, and ranked lists of potential causal services before remediation is needed. This preliminary analysis creates a prepared knowledge base that enables rapid automated remediation decisions while maintaining accuracy, as the system has already processed and validated the causal relationships in advance rather than making hasty decisions under pressure.
Data Source
AI summary
Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by a local controller associated with a DMC domain, a set of correlated services associated with an anomaly; obtaining a service dependency graph associated with the set of correlated services; generating a causal variance for each service using the correlated services and the service dependency graph; generating a weighted rank order of causal services based on the causal variance associated with each service, and the weighted rank order of causal services includes a portion of the services associated with an application associated with the anomaly; and performing remediation based on the weighted rank order of the causal services.