Performance and Cost Evaluation of Public Blockchain: An NFT Marketplace Case Study
Bibliographic record
Abstract
Non Fungible tokens (NFTs) are receiving unprecedented attention among digital creators and traders. This technology allows creators to certify their digital assets on blockchain as a decentralized, immutable, and transparent database. They can transfer the ownership of NFTs, which can easily be traced without the risk of manipulation. The trading volume for NFTs has surged to $10 billion in the third quarter of 2021. Although the high complexity of the consensus algorithm in blockchain ensures better security, it imposes higher transaction costs and limited scalability. To alleviate high transaction fees, energy inefficiency, and delays, tens of public blockchain platforms with different consensus protocols are being proposed as alternatives for NFT marketplaces. A crucial design choice for such an NFT marketplace is, in fact, to select the best public blockchain platform. In this work, we evaluate the cost and the performance of three public blockchain platforms, Fantom, Avalanche, and Polygon, in a use case for minting and transferring NFTs. We present machine learning models to predict the transaction cost and the throughput for the three platforms as decision parameters to choose the most appropriate platform. Our experimental results in terms of transaction fees and throughput demonstrate that the Polygon network is more efficient than the other two platforms.
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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 | 0.004 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".