An analysis of network-partitioning failures in cloud systems
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
We present a comprehensive study of 136 system failures attributed to network-partitioning faults from 25 widely used distributed systems. We found that the majority of the failures led to catastrophic effects, such as data loss, reappearance of deleted data, broken locks, and system crashes. The majority of the failures can easily manifest once a network partition occurs: They require little to no client input, can be triggered by isolating a single node, and are deterministic. However, the number of test cases that one must consider is extremely large. Fortunately, we identify ordering, timing, and network fault characteristics that significantly simplify testing. Furthermore, we found that a significant number of the failures are due to design flaws in core system mechanisms. We found that the majority of the failures could have been avoided by design reviews, and could have been discovered by testing with network-partitioning fault injection. We built NEAT, a testing framework that simplifies the coordination of multiple clients and can inject different types of network-partitioning faults. We used NEAT to test seven popular systems and found and reported 32 failures.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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