Cloud Fault Diagnosis Using Machine Learning Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large data centers face challenges in detecting faults and differentiating between performance issues and application faults due to dynamic shared resources and varying workloads in virtualized cloud environments, leading to increased management costs and performance deterioration.
Innovation Solution
A computer-implemented method and system for problem determination and diagnosis in shared dynamic clouds, which monitors virtual machines and physical servers, identifies symptoms of problems, and classifies events as cloud-based anomalies or application faults using a three-stage approach involving monitoring, event generation, and diagnosis engines, relying on expert-defined fault signatures and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual observation with pre-defined thresholds is used for fault detection, then implementation complexity is low, but fault detection accuracy and root cause identification capability deteriorate
Solution Approach 1:
The patent replaces manual observation mechanisms with automated machine learning-based detection systems. The monitoring engine automatically collects metrics, the anomaly detection engine uses machine learning models to identify patterns, and the diagnosis engine automatically determines root causes, eliminating the need for manual threshold-based analysis while significantly improving detection accuracy.
Solution Approach 2:
The system performs self-diagnosis by automatically analyzing monitoring data, identifying anomalies, and determining root causes without human intervention. The machine learning models continuously learn from data patterns, enabling the system to autonomously improve its detection accuracy over time without requiring manual recalibration or observation.
2Reliability
If monitoring data from multiple sources is collected, then fault detection capability improves, but data processing complexity and difficulty of analysis increase
Solution Approach 1:
The patent segments the data processing system into distinct functional modules: a monitoring engine that collects data from multiple sources, an anomaly detection engine that processes and analyzes the data using machine learning, and a diagnosis engine that determines root causes. This modular segmentation allows each component to handle specific aspects of data processing independently, reducing overall complexity while maintaining comprehensive fault detection capability.
Solution Approach 2:
The anomaly detection engine acts as an intermediary between raw monitoring data and final diagnosis conclusions. It processes and filters data from multiple sources, identifies patterns and anomalies, and presents simplified information to the diagnosis engine, thereby reducing the complexity of direct multi-source data analysis while maintaining comprehensive detection capability.
3Measurement precision
If shared resources are monitored to detect performance issues, then cloud anomaly detection improves, but differentiation between contention and application faults becomes more difficult
Solution Approach 1:
The patent applies local quality analysis by examining specific metrics and patterns at different levels (virtual machine level, host level, cluster level) to understand the local context of performance issues. The system analyzes local metric patterns, correlation between metrics, and temporal characteristics to differentiate between resource contention and application faults, improving accuracy while managing complexity through targeted local analysis rather than holistic examination.
Data Source
AI summary
Techniques for problem determination and diagnosis in a shared dynamic cloud environment. A method includes monitoring each virtual machine and physical server in the shared dynamic cloud environment for at least one metric, identifying a symptom of a problem and generating an event based on said monitoring, analyzing the event to determine a deviation from normal behavior, and classifying the event as a cloud-based anomaly or an application fault based on existing knowledge.