Scaling Smart: A Rigorous Framework for Selecting Optimal Layer 2 Blockchain Solutions
Bibliographic record
Abstract
Layer 1 blockchains like Bitcoin and Ethereum suffer from scalability issues, limiting their throughput and latency. In response, Layer 2 solutions have emerged for off-chain transaction processing to improve performance with distinct metrics, such as throughput, latency, transaction fees, etc. This paper proposes a rigorous decision-making framework for the evaluation and selection of Layer 2 blockchain solutions. The framework employs a multi-criteria decision-making (MCDM) approach, integrating key performance metrics, including throughput, latency, decentralization, data availability, adoption, transaction fees, and total value locked (TVL). First, we performed metric normalization to standardize evaluation criteria such as throughput, latency, decentralization, and transaction fees. Second, we collected real-world data for Layer 2 solutions, including Polygon, Optimism, Arbitrum, Starknet, and zkSync Era, and applied a weighted composite scoring mechanism. Third, we incorporated 25 distinct evaluation profiles to reflect diverse real-world application needs, such as prioritizing scalability, decentralization, or cost efficiency. Results across 25 profiles illustrate that preferences over metrics materially affect the ranking of L2s, underscoring the need for profile-aware selection rather than a one-size-fits-all choice. Sensitivity analysis further validated the robustness of the rankings by measuring the impact of varying parameter weights. This approach provides a transparent, data-driven tool for stakeholders, facilitating the selection of optimal Layer 2 solutions tailored to decentralized application requirements and advancing blockchain scalability.
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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 | 0.038 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".