Atomic Distributed Semaphores for Accessing Networked Data
Bibliographic record
Abstract
Distributed hash tables (DHTs), based on consistent hashing, offer efficient lookup services for decentralized distributed systems. DHTs operate efficiently to handle large number of network nodes with continual node arrivals, departures, and failures. Upon addressing the crucial issues of communication efficiency and offering load balancing in dynamic networking environments, DHTs are the essential components for building structured peer-to-peer (P2P) overlay networks. Although structured overlays improve data availability and consistency, they do not provide strong semantics on distributed data mutual exclusion operations. For a robust network operating system, it is essential to provide atomic data access semantic services. In this paper, a distributed semaphore (DISEM) mechanism is proposed, and it is designed on top of a dynamic structured overlay. The proposed design circumvents the availability and consistency issues. Independent of any underlying overlay algorithms, DISEM provides a tunable level of data availability and consistency, while offering fault tolerance and reliable delivery services. A testbed prototype has been implemented to validate the mutual exclusiveness of networked replicas under different traffic loadings. The measured results indicate that DISEM offers high mutual exclusive access rates under different networking conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".