FALL: A Large-Scale System Failure Prediction Method Based on Large Language Models

FALL (Prior Failure Detection in Large Scale System Based on Language Model) is a large-scale system failure prediction method based on large language models, which is the open-source implementation of the academic paper of the same name (published in IEEE TDSC). Its core idea is to use LLM to analyze system logs and achieve prior detection before failures occur, thereby improving system reliability. The project is maintained by oussamadjelloul, and the source code is available on GitHub (link: https://github.com/oussamadjelloul/FALL), with an update date of 2026-06-08.

Modern large-scale IT infrastructures (such as cloud services, distributed systems, and microservices) bring flexibility but also introduce complex failure modes, making traditional passive failure detection unable to meet the demand. Failure prediction technology aims to identify potential problems in advance, and LLM's pattern recognition and sequence modeling capabilities are suitable for handling operation and maintenance tasks like log analysis—system logs are sequential data, and LLM can learn normal patterns and identify context-related anomalies, which is superior to traditional rule-based or statistical methods.

FALL's technical architecture consists of three parts: 1. Log preprocessing: Extract templates and parameters through parsing (e.g., Drain, Spell tools), construct sequences by time window, and vectorize them; 2. LLM-based anomaly detection: Use pre-trained LLM to understand the semantics of log sequences and capture context-aware anomalies; 3. Failure prediction mechanism: Analyze log sequence trends, evaluate system health status, and issue early warnings before failures (need to balance the warning time window).

Compared with traditional methods: 1. Rule-based methods rely on manual rules and are difficult to cover all scenarios, while FALL does not require manual rules; 2. Statistical methods need to assume data distribution, while FALL can capture non-linear complex patterns; 3. Deep learning-based methods (e.g., LSTM) are mostly trained from scratch, while FALL uses the knowledge of pre-trained LLM and has stronger generalization ability.

FALL is applicable to multiple scenarios: 1. Cloud infrastructure monitoring: Early detection of potential problems in data centers; 2. Microservice operation and maintenance: Monitor service interaction logs and predict cascading failures; 3. Financial transaction systems: Support early switching to backup systems to reduce losses; 4. Industrial Internet of Things: Predict equipment failures and realize predictive maintenance.

Implementing FALL requires considering: 1. Computing resources: Balance model size and inference latency, requiring GPU acceleration; 2. Data privacy: Logs may contain sensitive information, requiring local deployment or desensitization; 3. False positives and false negatives: Need to tune the model to balance the two; 4. Interpretability: LLM's decision-making transparency is insufficient, so the ability to explain predictions needs to be improved.

Future development directions include: 1. Multimodal fusion: Integrate data sources such as logs, metrics, and traces; 2. Root cause analysis: Combine knowledge graphs and causal reasoning to achieve intelligent diagnosis; 3. Automatic repair: From prediction to automatic repair, improve the level of AIOps; 4. Federated learning: Use data from multiple organizations to improve the model under privacy protection.

FALL demonstrates the application potential of LLM in the field of system failure prediction, improving the reliability of large-scale systems through semantic understanding and pattern recognition. Such technologies represent the development direction of AIOps. With the maturity of LLM and the reduction of computing costs, more AI-driven intelligent operation and maintenance tools will emerge to help manage complex IT infrastructures.