Replica placement in data grid: considering utility and risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Grid computing emerges from the need to integrate a collection of distributed computing resources to offer performance unattainable by any single machine. Grid technology facilitates data sharing across many organizations in different geographical locations. Data replication is an excellent technique to move and cache data close to users. Replication reduces access latency and bandwidth consumption. It also facilitates load balancing and improves reliability by creating multiple data copies. However, grid environments introduce significant new challenges such as dynamic resource availability and network performance changes. As users requests vary constantly, the system needs a dynamic replication strategy that adapts to users' dynamic behavior. To address such issues, this paper presents and evaluates the performance of six dynamic replication strategies for two different kinds of access patterns. Our replication strategies are mainly based on utility and risk. Before placing a replica at a site, we calculate an expected utility and risk index for each site by considering current network load and user requests. A replication site is then chosen by optimizing expected utility or risk indexes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it