Performance and Cost Evaluation of Public Blockchain: An NFT Marketplace Case Study

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.