DAG Log Matching for Undesired Operation Root-Cause Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data processing systems face challenges in identifying and addressing undesired operations that hinder the provision of computer-implemented services, as existing methods are inefficient in detecting and remedying such issues.
Innovation Solution
A method involving the use of directed acyclic graphs (DAGs) to analyze log entries, identify offending signatures, and perform root cause analysis to determine and remediate undesired operations by generating corrective action sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods are used to detect and remedy undesired operations, then the system can maintain basic operational continuity, but the efficiency and speed of identifying and addressing issues are insufficient
Solution Approach 1:
The system performs preliminary actions by continuously monitoring log entries and maintaining a directed acyclic graph of offending signatures in advance. When undesired operations occur, the pre-prepared graph structure enables immediate identification and remediation without time-consuming analysis, thus resolving the contradiction between productivity and time loss.
2Measurement precision
If comprehensive log analysis is performed to identify undesired operations, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system creates a copied representation of log entry relationships in the form of a directed acyclic graph containing offending signatures. This graph copy allows for efficient pattern matching and accurate identification of undesired operations without requiring complex real-time analysis of all raw log data, thus resolving the contradiction between measurement precision and device complexity.
Data Source
AI summary
Methods and systems for managing operation of a deployment comprising data processing systems are disclosed. The operation may be managed by identifying an undesired operation in a data processing system. The undesired operation may be identified by obtaining the offending signature on a directed acyclic graph. The offending signature may be obtained by matching new log entries from a data processing system to a portion of log entries on the directed acyclic graph that are associated with the offending signature. From the log entries, problem contexts and correlation scores may be obtained. The problem contexts, the correlation scores and the offending signature may be used to find the root cause of the undesired operation.