Location-aware associated data placement for geo-distributed data-intensive applications
Why this work is in the frame
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Bibliographic record
Abstract
Data-intensive applications need to address the problem of how to properly place the set of data items to distributed storage nodes. Traditional techniques use the hashing method to achieve the load balance among nodes such as those in Hadoop and Cassandra, but they do not work efficiently for the requests reading multiple data items in one transaction, especially when the source locations of requests are also distributed. Recent works proposed the managed data placement schemes for online social networks, but have a limited scope of applications due to their focuses. We propose an associated data placement (ADP) scheme, which improves the co-location of associated data and the localized data serving while ensuring the balance between nodes. In ADP, we employ the hypergraph partitioning technique to efficiently partition the set of data items and place them to the distributed nodes, and we also take replicas and incremental adjustment into considerations. Through extensive experiments with both synthesized and trace-based datasets, we evaluate the performance of ADP and demonstrate its effectiveness.
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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 | 0.001 | 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 | 0.003 | 0.001 |
| 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