Adaptive Replica Placement in Hierarchical Data Grids
Bibliographic record
Abstract
Data grids support distributed data-intensive applications that need to access massive (multi-terabyte or larger) datasets stored around the world. Ensuring efficient and fast access to such widely distributed datasets is hindered by the high latencies of wide-area networks. To speed up access, data files can be replicated so users can access nearby copies. Replication also provides high data availability, decreased bandwidth consumption, increased fault tolerance, and improved scalability. Since a grid environment is highly dynamic, resource availability, network latency, and users requests may change frequently. To address these issues a dynamic replica placement strategy that adapts to dynamic behavior in data grids is needed. In this paper, we extend our earlier work on popularity-based replica placement proposing a new adaptive algorithm for use in large-scale hierarchical data grids. Our algorithm dynamically adapts the frequency and degree of replication based on data access arrival rate and available storage capacities. We evaluate our algorithm using OptorSim. Our results show that our algorithm can shorten job execution time greatly and reduce bandwidth consumption compared to its non-adaptive counterpart which outperforms other existing replica placement methods.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".