Study of Different Replica Placement and Maintenance Strategies in Data Grid
Bibliographic record
Abstract
Data replication is an excellent technique to move and cache data close to users. By replication, data access performance can be improved dramatically. One of the challenges in data replication is to select the candidate sites where replicas should be placed. We use a multi-objective model to address the replica placement problem. The multi-objective model considers the objectives of p-median and p-center models simultaneously to select the candidate sites that will host replicas. The objective of the p-median model is to find the locations of p possible candidate replication sites by optimizing total (or average) response time; where the p-center model finds p candidate sites by optimizing maximum response time. A grid environment is highly dynamic so user requests and network latency vary constantly. Therefore, candidate sites currently holding replicas may not be the best sites to fetch replica on subsequent requests. We propose a dynamic replica maintenance algorithm that re-allocates to new candidate sites if a performance metric degrades significantly over last K time periods. Simulation results demonstrate that the dynamic maintenance algorithm with multi-objective static placement decision performs best in dynamic environments like data grids.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".