DHT overlay schemes for scalable p-range resource discovery
Bibliographic record
Abstract
The information service is a critical component of a grid infrastructure for resource discovery. Although P2P computing paradigm could address some of the scalability issues that plagued grid resource discovery, most existing distributed hash table (DHT) based P2P overlays have difficulty in treating attribute range queries that are common in resource discovery lookups due to the inherent randomness of hash functions. Recently, there have been various attempts to solve the range search problem over DHT networks [Aberer, et al. (2003), Gao and Steenkiste (2004), Ratnasamy, et al. (2003), and Tanin, et al. (2005)]. Central to all of these is a mapping scheme which maps the tree-structured logical index space to some DHT-based physical node space. In this paper, we propose a general framework to put all these under the same umbrella based on how mapping of the tree-structured index that identifies a physical node responsible of a particular range is done through replication. We identify three schemes which should cover the spectrum of all meaningful replication schemes: the tree replication scheme (TRS) replicates the entire tree; the path caching scheme (PCS) replicates paths from root to leaves; and the node replication scheme (NRS) replicates individual logical nodes.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".