Causal Model for Distributed System Performance Detection
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Solution Overview
Problem
Current methods fail to provide an automated and comprehensive approach to detect latent performance degradations and build dependency models for distributed systems, especially in complex network environments, as they are either computationally heavy, costly, or limited to detecting hard faults rather than performance issues.
Innovation Solution
A method and system that uses a controlled cloud environment with a sandbox to perturb resources, measure responses, identify correlations, and build causal models to detect and model performance degradations and failures in a distributed application, enabling automated testing and root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated test environments and tools are used to stimulate distributed applications, then system dependency discovery and performance degradation detection improve, but computational complexity and implementation cost increase
Solution Approach 1:
The system segments the distributed application into individual nodes and their dependencies, analyzing each node separately through controlled stimuli application. This segmentation allows automated dependency discovery without requiring complex global analysis of the entire system at once, thereby reducing computational complexity while maintaining automation.
Solution Approach 2:
The system performs preliminary actions by applying controlled stimuli to nodes before actual performance degradation occurs. This proactive approach allows the system to map dependencies and identify potential failure points in advance, enabling automated detection without requiring complex real-time analysis during actual failures.
2Measurement precision
If comprehensive behavioral modeling is performed to detect latent performance degradations, then detection precision improves, but measurement and analysis difficulty increase
Solution Approach 1:
The system introduces an intermediary layer that observes and measures node behaviors indirectly through controlled stimuli responses. This intermediary approach allows precise detection of performance degradations by measuring how nodes respond to standardized inputs, avoiding the need for complex direct analysis of internal system states while maintaining high detection precision.
Solution Approach 2:
The system changes parameters by applying controlled stimuli that modify node operating conditions temporarily. By observing how nodes respond to these parameter changes, the system can precisely detect performance degradations and map dependencies without requiring complex continuous monitoring of all system parameters under normal operation.
3Measurement precision
If controlled environment with stimuli application is used, then causal relationship identification improves, but system invasiveness increases
Solution Approach 1:
The system applies partial actions by using controlled stimuli that affect only specific nodes or resources temporarily, rather than impacting the entire system continuously. This approach enables accurate causal relationship identification through targeted measurements while minimizing overall system invasiveness, as the stimuli are applied selectively and transiently rather than globally and permanently.
Data Source
AI summary
In one embodiment, the method includes determining one or more nodes associated with a treatment of a query; generating one or more stimuli associated with the treatment of the query wherein the or each stimulus are likely to perturb one or more resources within a system; measuring data at the or each node relating to the resources to determine the effect of the or each stimuli at the or each node; identifying one or more pairs of nodes which have a correlation in the measured data; transforming the correlation into a causal relationships where the cause is a measuring device measuring the response and the consequences are the other correlated measuring devices; generating a list of causal relationships; and combining different causal relationships into a causal model so that a chain of causal propagations can be built.