Multi-Modal Job Failure Attribution for Distributed Nodes
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Solution Overview
Problem
Existing methods for attributing job failures in distributed computing systems are limited by their reliance on regular expressions and handcrafted rules, which fail to generalize to new failures and abnormal phenomena, and lack integration of data from multiple sources, leading to inaccurate failure identification and inefficient resource management.
Innovation Solution
A multi-modal attribution approach using importance matrices and anomaly detection for log and time-series data, combined with a large language model (LLM) to analyze and attribute job failures to specific nodes, providing evidence and suggested actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If regular expressions and handcrafted rules are used for failure attribution, then implementation is simple, but coverage and accuracy are limited and cannot generalize to new failures
Solution Approach 1:
The patent replaces traditional mechanical rule-based systems (regular expressions and handcrafted rules) with an unsupervised machine learning system that automatically learns patterns from multi-modal data. This substitution enables the system to generalize to new failure types without requiring manual rule updates, directly resolving the contradiction between implementation simplicity and adaptability.
Solution Approach 2:
The system changes the fundamental parameters of failure analysis by transitioning from static rule-based parameters to dynamic, data-driven parameters learned from multi-modal logs and time-series data. This allows the system to adapt to new failure patterns while maintaining automated operation, addressing both the need for simplicity and generalization.
2Device complexity
If single-source data is used for analysis, then data processing is simple, but attribution accuracy is limited
Solution Approach 1:
The patent merges multiple data sources and modalities (system logs, application logs, time-series metrics, and trace data) into a unified analysis framework. This combination enriches the feature space for failure attribution, improving accuracy while the unsupervised learning approach automatically handles the complexity of integrating these diverse data types.
Solution Approach 2:
The system creates a universal attribution framework that can process multiple data modalities through a single unsupervised learning model. This multi-functional approach enables accurate attribution across different failure types and data sources without requiring separate processing pipelines for each modality.
3Adaptability or versatility
If supervised learning is used, then learning capability is strong, but ground truth labels are expensive and hard to obtain
Solution Approach 1:
The patent implements self-service learning through unsupervised anomaly detection that automatically identifies failure patterns without requiring external ground truth labels. The system learns from the inherent structure and statistical properties of the multi-modal data itself, eliminating the need for expensive and difficult-to-obtain labeled datasets while maintaining strong learning capability.
Solution Approach 2:
Instead of using supervised learning that requires labels to teach the system what is normal vs. abnormal, the patent inverts the approach by using unsupervised learning to automatically discover anomalies from unlabeled data. This inversion eliminates the label acquisition bottleneck while preserving adaptive learning capabilities.
Data Source
AI summary
Approaches presented herein provide for attribution of fault for a failure of a computing job performed by a distributed set of resources. Different types of data can be analyzed for different modalities, such as text or time series data from compute, networking, and/or storage resources used to perform the computing job. This can include, for example, performing statistical analysis or anomaly detection to identify potentially responsible resources. A trained language model can analyze information and evidence for the potentially responsible resources, and can generate an attribution report identifying the resources that were likely responsible for the failure, along with an explanation and one or more recommended remediation actions.