Causal Graph Anomaly Detection for Device Resource Bottlenecks
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Solution Overview
Problem
Existing techniques for improving computing device performance are limited by predefined rules and lack dynamic evaluation methods, making it difficult to identify and address performance issues effectively, especially in complex scenarios where multiple possible causes exist.
Innovation Solution
A method utilizing a causal graph to connect performance issue types, metrics, and resources, combined with anomaly detection and machine learning, to automatically identify resource bottlenecks and suggest configuration adjustments, thereby enhancing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined rules are used to map software applications to configurations, then performance improvement is achieved for evaluated applications, but the system cannot dynamically evaluate or support new applications
Solution Approach 1:
The system enables self-service by allowing new applications to automatically generate their own performance profiles through autonomous evaluation. The evaluation engine dynamically assesses application behavior without requiring preconfiguration, and the application itself provides the necessary performance data through instrumented code execution, eliminating the need for manual laboratory evaluation.
Solution Approach 2:
The system transitions from static predefined rules to dynamic evaluation by continuously monitoring application performance metrics during execution. The performance profile is not fixed but adapts based on real-time observations of application behavior, resource consumption patterns, and system state changes, enabling the system to handle diverse and evolving application requirements.
2Measurement precision
If manual evaluation methods are used to identify performance issues, then detailed analysis is possible, but time and costs increase significantly
Solution Approach 1:
The system replaces manual mechanical evaluation processes with automated electronic monitoring and analysis. Instrumented code execution automatically collects performance data, the evaluation engine processes metrics through algorithmic analysis, and machine learning models identify patterns and root causes, substituting human analyst time with automated computational processes that maintain or improve detection accuracy.
Solution Approach 2:
The system introduces an intermediary evaluation engine that acts as a mediator between application execution and performance analysis. This engine instruments the code, collects metrics, and applies evaluation rules automatically, serving as an intermediate layer that enables precise measurement without requiring direct manual intervention at each measurement point.
3Measurement precision
If comprehensive performance monitoring is implemented, then accurate root cause identification is achieved, but system complexity increases
Solution Approach 1:
The system segments the performance monitoring function into distinct modular components: instrumentation modules that collect specific metric types, an evaluation engine that processes metrics according to defined rules, and a profile generation module that synthesizes results. This segmentation allows each component to be independently developed, maintained, and scaled, reducing overall system complexity while maintaining comprehensive monitoring capability.
Data Source
AI summary
Techniques are provided for detection of resource bottlenecks in computing devices. A particular resource bottleneck can be identified as a cause of a current device issue based on a dynamic evaluation, by an anomaly detection module, of performance metrics of a computing device. Once a particular resource bottleneck is identified as anomalous, one or more corresponding adjustments to configuration settings for the computing device can be identified to mitigate the current device issue using a causal graph that represents the dependencies among (i) various issue types for a computing device, (ii) performance metrics of the computing device to evaluate for each issue type, and (iii) one or more resources that may be a cause of a given issue type. The corresponding adjustments to the computing device can be automatically identified based on the resource determined to be the cause of the given device issue type.