Caelus: Verifying the Consistency of Cloud Services with Battery-Powered Devices
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
Cloud storage services such as Amazon S3, Drop Box, Google Drive and Microsoft One Drive have become increasingly popular. However, users may be reluctant to completely trust a cloud service. Current proposals in the literature to protect the confidentiality, integrity and consistency of data stored in the cloud all have shortcomings when used on battery-powered devices -- they either require devices to be on longer so they can communicate directly with each other, rely on a trusted service to relay messages, or cannot provide timely detection of attacks. We propose Caelus, which addresses these shortcoming. The key insight that enables Caelus to do this is having the cloud service declare the timing and order of operations on the cloud service. This relieves Caelus devices from having to record and send the timing and order of operations to each other -- instead, they need to only ensure that the timing and order of operations both conforms to the cloud's promised consistency model and that it is perceived identically on all devices. In addition, we show that Caelus is general enough to support popular consistency models such as strong, eventual and causal consistency. Our experiments show that Caelus can detect consistency violations on Amazon's S3 service when the desired consistency requirements set by the user are stricter than what S3 provides. Caelus achieves this with a roughly 12.6% increase in CPU utilization on clients, 1.3% of network bandwidth overhead and negligible impact on the battery life of devices.
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 | 0.000 |
| 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