Cloud Infrastructure Proactive Troubleshooting via Rule Learning Engines
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Solution Overview
Problem
Current cloud management tools struggle to efficiently detect and troubleshoot performance problems in cloud infrastructure, leading to time-consuming and error-prone processes that are costly for application owners.
Innovation Solution
An automated computer-implemented method and system that uses a graphical user interface to enable users to select key performance indicators (KPIs) for cloud infrastructure components, deploying separate rule learning engines to generate rules for detecting problems, and automatically executing remedial measures to resolve issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual troubleshooting processes are used by teams of engineers, then problems can be diagnosed and resolved, but the process becomes time-consuming and expensive
Solution Approach 1:
The system performs preliminary actions by proactively detecting performance degradation trends before they become critical failures. The proactive problem detection mechanism continuously monitors cloud infrastructure metrics and identifies emerging issues, allowing remediation to be initiated before complete system failure occurs, thus reducing troubleshooting time while maintaining reliability
Solution Approach 2:
The system enables self-service by implementing automated root cause analysis and remediation execution. The intelligent system autonomously analyzes performance data, identifies root causes without human intervention, and executes predetermined remediation actions, eliminating the need for manual troubleshooting by engineering teams and significantly reducing both time and cost
2Reliability
If manual troubleshooting processes are used by teams of engineers, then problems can be diagnosed and resolved, but the cost increases due to lost revenue and engineering resources
Solution Approach 1:
The system enables self-service by implementing automated root cause analysis and remediation execution. The intelligent system autonomously analyzes performance data, identifies root causes without human intervention, and executes predetermined remediation actions, eliminating the need for manual troubleshooting by engineering teams and significantly reducing both time and cost
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring cloud infrastructure performance metrics and using this information to drive automated remediation. The closed-loop system learns from performance data, adjusts its detection algorithms, and refines remediation strategies over time, improving reliability while reducing the need for expensive human engineering resources
3Difficulty of detecting and measuring
If cloud management tools generate alerts, then performance problems can be detected, but the alerts cannot be understood or used to diagnose the underlying problem
Solution Approach 1:
The system introduces an intermediary layer between performance monitoring and diagnostic analysis. The intelligent platform acts as a mediator that translates raw performance metrics and alerts into meaningful diagnostic information by correlating multiple data sources, identifying patterns, and presenting actionable insights that bridge the gap between detection and diagnosis
Solution Approach 2:
The system replaces the mechanical process of manual alert analysis with intelligent automated analysis. Machine learning algorithms and analytical engines substitute for human engineer analysis, automatically interpreting alerts, correlating performance data, and generating diagnostic conclusions, thereby preventing loss of diagnostic information while improving detection capability
4Adaptability or versatility
If knowledge gained by engineering teams is not shared, then individual team expertise is maintained, but subsequent teams cannot efficiently diagnose similar problems
Solution Approach 1:
The system implements universality by creating a centralized knowledge repository that serves multiple engineering teams and applications. The platform captures diagnostic knowledge, root cause analyses, and remediation procedures in a universal format that can be applied across different cloud infrastructure environments and problem scenarios, enabling any team to benefit from previously gained knowledge and significantly reducing diagnosis time for similar problems
Data Source
AI summary
Automated computer-implemented methods and systems for troubleshooting and resolving problems with objects of a cloud infrastructure are described herein. In response to detecting abnormal behavior of an object running in the cloud infrastructure based on a key performance indicator (“KPI”) of the object, a graphical user interface (“GUI”) is displayed to enable a user to select KPIs of components of the object. For each of the components, a separate rule learning engine is deployed to generate rules for detecting a problem with the component based on the KPI of the object and the KPIs of the component. The rules are subsequently used to detect a runtime problem with the object and display in the GUI remedial measures for resolving the problem. Remedial measures are automatically executed to resolve the problem with the object via the GUI.