Data Decomposition Based Partial Replication Model for Software Services
Bibliographic record
Abstract
Nowadays many software services are hosted in the Cloud. When there are more requests on these services, there are also more queries sent to the underlying database. In order to keep up with the increasing workload, it is necessary to have multiple servers hosting the data. Some cloud providers offer the full data replication solution. However, this solution only works when the load mainly consists of the read requests, and when the number of write requests increases, it does not scale well. Although data decomposition has been widely used in data-intensive web sites, not much study has been done on how to decompose the underlying data of software services for the purpose of data replication. In this paper, we propose a data-decomposition-based partial replication model for software services. We devise an automatic algorithm for data decomposition under the constraint of the capacity limit of the host machines. We evaluate our approach from two aspects: scalability and performance, using two benchmarks: RUBiS and TPC-W. In the experiment, we test the algorithm using different workload inputs, and also compare our approach with the full data replication approach.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".