Leveraging Large Language Models for the Auto-remediation of Microservice Applications: An Experimental Study
Bibliographic record
Abstract
Runtime auto-remediation is crucial for ensuring the reliability and efficiency of distributed systems, especially within complex microservice-based applications. However, the complexity of modern microservice deployments often surpasses the capabilities of traditional manual remediation and existing autonomic computing methods. Our proposed solution harnesses large language models (LLMs) to generate and execute Ansible playbooks automatically to address issues within these complex environments. Ansible playbooks, a widely adopted markup language for IT task automation, facilitate critical actions such as addressing network failures, resource constraints, configuration errors, and application bugs prevalent in managing microservices. We apply in-context learning on pre-trained LLMs using our custom-made Ansible-based remediation dataset, equipping these models to comprehend diverse remediation tasks within microservice environments. Then, these tuned LLMs efficiently generate precise Ansible scripts tailored to specific issues encountered, surpassing current state-of-the-art techniques with high functional correctness (95.45%) and average correctness (98.86%).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".