Cloud Infrastructure State Modeling for Automated Root-Cause Diagnostics
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Solution Overview
Problem
The complexity of cloud ecosystems with dependencies between components necessitates extensive time and skilled personnel for health check operations, leading to inefficient diagnostics and maintenance in cloud-based infrastructure.
Innovation Solution
A cloud ecosystem state model facilitates automated health checks, data collection, and analytical tools that simplify and accelerate diagnostics, providing accurate recommendations and automatic adaptation to diagnostic data fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual health check operations are performed on cloud ecosystems, then diagnostic accuracy can be maintained through skilled personnel analysis, but the process requires extended time and highly skilled personnel
Solution Approach 1:
The system performs self-diagnosis by automatically executing health check operations against cloud ecosystem components, collecting diagnostic data, and analyzing results without requiring skilled personnel intervention. The automated diagnostic engine executes checks, gathers metrics, and generates recommendations independently, enabling the system to service itself.
Solution Approach 2:
Manual analysis by skilled personnel is replaced with an automated diagnostic engine that uses computational algorithms to analyze cloud ecosystem states. The system substitutes human expertise with automated processing capabilities, including machine learning models and rule-based analysis, to interpret diagnostic data and generate recommendations.
2Measurement precision
If manual health check operations are performed on cloud ecosystems, then diagnostic thoroughness can be maintained through skilled personnel, but the process requires highly skilled personnel capable of determining root causes
Solution Approach 1:
The system autonomously determines root causes by automatically analyzing diagnostic data patterns, correlating component states, and identifying causal relationships without human intervention. The automated engine processes complex inter-component dependencies and generates actionable recommendations independently.
Solution Approach 2:
The system incorporates feedback mechanisms where diagnostic results are continuously monitored, analyzed, and used to generate recommendations. The automated engine learns from diagnostic patterns and refines its analysis, providing feedback loops that improve root cause determination accuracy over time through adaptive algorithms.
3Productivity
If cloud ecosystem state modeling is used for automated diagnostics, then diagnostic speed is accelerated, but the system complexity increases due to state model management
Solution Approach 1:
The cloud ecosystem state model is segmented into modular components representing different parts of the cloud infrastructure (compute, storage, network, applications). Each component has its own state representation and health check parameters, allowing the complex system to be managed through modular, independent units that can be processed and analyzed separately.
Data Source
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AI summary
A cloud infrastructure diagnostics system comprises a cloud state configuration module operative to define a cloud state model with respect to a cloud infrastructure, wherein the cloud state model comprises a structured collection of selected operational characteristics relative to the cloud infrastructure components. The cloud state model may include a definition of dependencies between the cloud infrastructure components where applicable. A cloud state monitoring module is operative, responsive to the cloud state model definition, to collect periodic cloud state updates with respect to the cloud infrastructure. A cloud state analysis module is operative, responsive to receiving the periodic cloud state updates, to perform: comparing one or more cloud state updates to a corresponding predefined set of reference states; and determining one or more notifications to be transmitted with respect to the operational characteristics of the cloud infrastructure components.