Cloud Service Impact Analysis via CNF and DAG Root Cause Detection
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Solution Overview
Problem
In cloud computing environments, identifying the root cause of failures across multiple interacting devices or components is challenging, leading to difficulties in providing effective remediation and maintaining service level agreements due to the lack of efficient correlation and communication between different teams responsible for various components.
Innovation Solution
A method involving the creation of a relationship tree to aggregate component events, conversion of these events into conjunctive normal form statements, and the use of directed acyclic graphs to determine true values and provide remediation solutions, facilitating root cause analysis and service impact assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of failure logs by multiple teams is used, then each team can review their own component failures, but it becomes difficult to correlate failures across components and identify root causes
Solution Approach 1:
The patent segments the complex failure analysis problem into distinct components: (1) collecting failure events from multiple components, (2) creating propositions representing failure states, (3) converting to CNF logical form, and (4) using DAGs to traverse and identify root causes. This segmentation allows systematic handling of complexity while maintaining correlation accuracy.
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a mediator between multiple component teams. This intermediary collects events from all components, processes them through logical frameworks (propositions, CNF, DAGs), and produces unified root cause analysis, eliminating the need for manual cross-team coordination while maintaining accurate failure correlation.
2Productivity
If information is exchanged between multiple teams manually, then communication between teams occurs, but the process is time-consuming and solutions are not readily available
Solution Approach 1:
The patent implements preliminary action by pre-establishing the logical framework (propositions, CNF statements, and DAG structures) before failures occur. When failures happen, the system immediately traverses the pre-built DAGs to identify root causes, eliminating the time-consuming manual information exchange and enabling rapid solution delivery.
Solution Approach 2:
The patent replaces the mechanical system of manual information exchange between teams with an automated electronic system. The automated system collects events, processes them through logical frameworks, and generates solutions without human intervention, dramatically increasing productivity while reducing troubleshooting time.
3Reliability
If no automated correlation system is used, then manual review processes are simple to implement, but root cause analysis and service level agreement support are difficult to provide
Solution Approach 1:
The patent implements feedback mechanisms where the automated system continuously monitors component events, processes them through logical frameworks, and provides actionable root cause analysis and remediation recommendations. This feedback loop ensures reliable service level agreement compliance by systematically identifying and addressing failures, while the increasing automation handles the complexity of multi-component correlation.
Data Source
AI summary
Described herein are methods and a system for analyzing the impact of multiple components with one another that support a cloud service. Events are collected in time series from the components and aggregated in a relationship tree that groups the components. Propositions as to the events are created from which a conjunctive normal form (CNF) statement is derived. The CNF statement is converted to one or more directed acyclic graphs (DAG). The DAGs are traversed to determine TRUE values used to provide remediations solutions.