Maze-Driven Self-Diagnostics for Cloud Microservices
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Solution Overview
Problem
The complexity of cloud-based systems with numerous microservices poses challenges in troubleshooting, as traditional knowledge-based approaches require human intervention and are limited by the distributed nature of cloud environments, leading to inefficiencies in identifying failure points and accumulating knowledge.
Innovation Solution
A maze-driven self-diagnostic system using reinforcement learning models the software application as a two-dimensional array of components and states, applying a path-finding process to determine success paths and compare them with failure cases to identify potential causes of failures, reducing human intervention and enabling automated diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional knowledge-based troubleshooting approaches are used, then human expertise can identify failure points, but the process requires substantial human intervention and is limited by the distributed nature of cloud environments
Solution Approach 1:
The system enables automated self-diagnosis by training machine learning models to independently identify failure points in microservice architectures. The models process system states, transition probabilities, and observation data to automatically determine failure locations without human intervention, allowing the system to serve itself in troubleshooting tasks.
Solution Approach 2:
The patent replaces manual human troubleshooting processes with automated machine learning-based diagnostic systems. The mechanical interaction of human analysts examining system states is substituted with computational models that process the same information through learned patterns and probability calculations, achieving both automation and maintained accuracy.
2Loss of time
If human experts manually troubleshoot cloud systems, then knowledge can be applied to identify failures, but knowledge accumulation is limited and time-consuming
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on historical troubleshooting data and system behavior patterns before actual failures occur. This advance preparation enables the models to quickly diagnose failures in real-time without requiring human experts to manually analyze each new failure scenario from scratch, significantly reducing troubleshooting time while accumulating knowledge in the trained models.
Solution Approach 2:
The patent transforms static human expertise into dynamic, adaptable machine learning models that can process and learn from multiple parameters including system states, transition probabilities, and observation data. This parameter-based approach allows the system to accumulate knowledge across diverse failure scenarios and adapt to new patterns, improving both speed and knowledge accumulation efficiency.
3Reliability
If comprehensive system monitoring is implemented to enable automated diagnosis, then failure identification can be improved, but system complexity increases
Solution Approach 1:
The patent segments the complex cloud system into discrete microservice components, each with defined states and transition probabilities. By dividing the overall system into manageable segments that can be independently modeled and analyzed, the system achieves reliable failure diagnosis without being overwhelmed by the complexity of the entire distributed architecture. Each segment can be troubleshooted independently while maintaining context of the whole system.
Data Source
AI summary
Systems and methods are provided for automatedly troubleshooting a computing application (e.g., a cloud-based computing application). An application domain of the computing application is modeled as a two-dimensional array of cells, a first dimension of the array representing components or microservices of the application domain, and a second dimension of the array representing states of the components or microservices, the array including paths between pairs of cells in the array. A troubleshooting goal is defined as a target state of the application domain, the target state corresponding to a target cell in the array. An initial state of the application domain is also provided, the initial state corresponding to an initial cell in the array. A reinforcement-learning-trained machine-learning algorithm can determine a solution path in the array between the initial cell and the target cell. Divergence between a failure case and a solution path indicates a probable failure cause.