QoS-Aware Distributed Replica Placement in Hierarchical Data Grids
Bibliographic record
Abstract
Data Grids provide services and infrastructure for distributed data-intensive applications accessing massive geographically distributed datasets. An important technique to speed access in Data Grids is replication, which provides nearby data access. Much of the work on the replica placement problem has focused on average system performance and ignored quality assurance issues. In a data grid environment, resource availability, network latency, and users' requests may change. Moreover, different sites may have different service quality requirements. In this paper, we introduce a new highly distributed and decentralized replica placement algorithm for hierarchical Data Grids that determines the positions of a minimum number of replicas expected to satisfy certain quality requirements. Our placement algorithm exploits the data access history for popular data files and computes replica locations by minimizing overall replication cost (read and update) while maximizing QoS satisfaction for a given traffic pattern. The problem is formulated using dynamic programming. We assess our algorithm using OptorSim. A comparison between our algorithm and its QoS-unconstrained counterpart shows that our algorithm can shorten job execution time greatly while consuming moderate bandwidth for data transfer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".