Bibliographic record
Abstract
This paper presents the AdaptCache project. AdaptCache is an adaptive caching middleware for application servers that monitors the current workload and generates policies to distribute and/or replicate objects and requests among the local caches of application servers so that most requests can be executed on locally cached objects and, at the same time, the load will be evenly distributed among servers. The project is divided into two main phases. The first one, which is described in detail in this paper, tackles the problem of dynamically distributing objects and requests for volatile and fluctuating e-commerce applications. Several data distribution approaches based on graph partitioning are proposed. The approaches are compared using the YCSB and RUBiS benchmarks showing that AdaptCache is able to dynamically capture various workload changes and react quickly to these changes. The second phase of the AdaptCache project explores data replication for distributed object caches. It discusses the advantages of object replication such as increased locality but also possible overheads due to consistency requirements and space limitations. Any dynamic replication solution must take these issues into account.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".