On Dependability of Heterogeneous Distributed Oracle System in Blockchain
Bibliographic record
Abstract
Both blockchain and oracle are among key technologies which are leveraged in Web 3.0 to empower the internet industry. Oracle aims to provision blockchain with real world data and support external connectivity for closed blockchain systems. Compared with a centralized oracle system, a distributed oracle system can tackle the issues of single point failure and untrusted data. This paper explores analytical modeling techniques to quantitatively study the dependability (availability and reliability) of the distributed oracle system with arbitrary number of heterogeneous oracle nodes. We first develop a Markov model to describe oracle system dynamics. Then we derive both the formula of system availability and the formula of mean time to failure (MTTF) to study the system reliability. The experimental results indicate 1) the system availability is mainly affected by mean node failure time when it is smaller than 10 days, 2) the system recovery ability has critical impact on availability, and 3) MTTF can be significantly improved by introducing more nodes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
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".