Multi-Modal Job Failure Attribution in Distributed Computing
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Solution Overview
Problem
Existing methods for attributing job failures in distributed computing systems rely on regular expressions and handcrafted rules, which lack generalization to new failures and abnormal phenomena, and require ground truth labels, limiting their effectiveness in identifying the root cause of job failures.
Innovation Solution
An unsupervised approach using multiple data sources and modalities, including log data and time-series data, combined with importance matrices and anomaly detection, to generate an attribution report through a large language model (LLM) that identifies root cause nodes and suggests remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If regular expressions and handcrafted rules are used for failure attribution, then the method is simple to implement, but the coverage and generalization capability are limited
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 manual rule updates, directly resolving the contradiction between implementation simplicity and adaptability.
Solution Approach 2:
The system changes the fundamental parameters of failure attribution by transitioning from static rule-based parameters to dynamic, data-driven parameters. By using unsupervised learning to automatically adapt parameters based on observed data patterns, the system achieves both ease of implementation (automatic adaptation) and high coverage (generalization to new failures).
2Measurement precision
If supervised learning methods are used, then the attribution accuracy can be improved, but the requirement for expensive ground truth labels limits their use
Solution Approach 1:
The patent implements self-service by using unsupervised learning algorithms that automatically learn from the data itself without requiring external ground truth labels. The system serves its own training needs by identifying patterns and anomalies inherent in the multi-modal data, eliminating the need for expensive labeled datasets while maintaining high attribution accuracy.
Solution Approach 2:
The system introduces an intermediary unsupervised learning model that bridges the gap between raw data and accurate attribution without requiring ground truth labels. This intermediary learns latent patterns and relationships in the data, enabling accurate failure attribution while avoiding the need for expensive supervised training data.
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.