Topology Sensitive Replica Selection
Bibliographic record
Abstract
As the disks typically found in personal computers grow larger, protecting data by replicating it on a collection of "peer" systems rather than on dedicated high performance storage systems can provide comparable reliability and availability guarantees but at reduced cost and complexity. In order to be adopted, peer-to-peer storage systems must be able to replicate data on hosts that are trusted, secure, and available. However, recent research has shown that the traditional model, where nodes are assumed to have identical levels of trust, to behave independently, and to have similar failure modes, is over simplified. Thus, there is a need for a mechanism that automatically and efficiently selects replica nodes from a large number of available hosts with varying capabilities and trust levels. In this paper we present an algorithm to handle replica node selection either for new replica groups or to replace failed replicas in a peer-to-peer storage system. We show through simulation that our algorithm maintains the node inter-connection topology minimizing the cost of recovery from a failed replica, measured by the number of nodes affected by the failure and the number of inter-node messages
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".