Failure Avoidance through Fault Prediction Based on Synthetic Transactions
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
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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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 | 0.000 | 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