Evaluation of Integration of ACBL and AOCC Caching Algorithms
Bibliographic record
Abstract
Transactional caching algorithms proposed for object database management systems (ODBMSs) have been classified into detection and avoidance categories, depending on whether they allow access to stale data. Although studies have shown that in most situations the leading detection-based algorithms tend to outperform those that use avoidance, most ODBMSs use a variation of the leading avoidance-based algorithm because detection algorithms in certain situations lead to abort rates that are unacceptable for some, typically interactive, applications. The Interoperable Server-side Caching (ISCT) algorithm allows both types of algorithms to interoperate in the same environment while being supported by an interoperable server. Clients use either the leading detection-based algorithm, called Adaptive Optimistic Concurrency Control (AOCC), or the leading avoidance-based algorithm, called Adaptive Call-Back Locking (ACBL). The caching operations of the client-side algorithms are not affected - only the server-side is adapted to interoperate with both types of clients. This paper presents the results of performance evaluation that compares the three algorithms under various loads. Evaluation shows that the interoperable server-side caching algorithm increases throughput in environments where some applications can use the high-throughput AOCC algorithm while other applications use a detection-based ACBL algorithm. To compare the algorithms a new workloads, in which different applications exhibit different server load characteristics, were developed.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".