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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".