Towards a middleware design for efficient blockchain oracles selection
Bibliographic record
Abstract
Blockchain smart contracts can only operate on data available on-chain, and face a major challenge of not being able to communicate with the world outside their own network. Blockchain oracles solve this problem by bridging the gap between on-chain and off-chain data. Chainlink is one of the most widely used decentralized oracle network with competing independent data providers with distinct characteristics in terms of price, performance, security, trust, etc. As a Chainlink service purchaser with unique a service level agreement (SLA) proposal for each job, a random selection of oracles to execute a certain job can result in sub-optimal solution, that could be alleviated by another oracle data provider. Therefore, in this work, we propose a middleware design to select the best data providers available in Chainlink oracle network based on individual job's service requirements. We model the oracle selection problem as a decision optimization problem and prove it to be NP-hard. We also propose two heuristic algorithms to approximate a solution to the modelled problem. We experimentally evaluate and compare the proposed design, and conclude that the proposed middleware architecture efficiently selects the oracles in terms of performance parameters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".