Failure Avoidance through Fault Prediction Based on Synthetic Transactions
Bibliographic record
Abstract
Abstract — System logs are an important tool in studying the conditions (e.g., environment misconfigurations, resource status, erroneous user input) that cause failures. However, production system logs are complex, verbose, and lack structural stability over time. These traits make them hard to use, and make solutions that rely on them susceptible to high maintenance costs. Additionally, logs record failures after they occur: by the time logs are investigated, users have already experienced the failures ’ consequences. To detect the environment conditions that are correlated with failures without dealing with the complexities associated with processing production logs, and to prevent failure-causing conditions from occurring before the system goes live, this research suggests a three step methodology: i) using synthetic transactions, i.e., simplified workloads, in pre-production environments that emulate user behavior, ii) recording the result of executing these transactions in logs that are compact, simple to analyze, stable over time, and specifically tailored to the fault metrics of interest, and iii) mining these specialized logs to understand the conditions that correlate to failures. This allows system administrators to configure the system to prevent these conditions from happening. We evaluate the effectiveness of this approach by replicating the behavior of a service used in production at Microsoft, and testing the ability to predict failures using a synthetic workload on a 650 million events production trace. The synthetic prediction system is able to predict 91 % of real production failures using 50-fold fewer transactions and logs that are 10,000-fold more compact than their production counterparts. Keywords-Failure prediction; failure avoidance; system logs; synthetic transactions; data analysis; data mining. I.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".