Leveraging Large Language Models for the Auto-remediation of Microservice Applications: An Experimental Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it