Abstract
Non-Fungible Token (NFT) has gained worldwide attention, relying on their superior data representation, as a solution for describing complex digital assets. However, the scale of NFT content data is so huge that we have to use P2P storage network to store it. Due to the lack of management capabilities of global resource allocation, retrieving content in existing P2P storage network relies heavily on distributed collaboration and cannot support low-latency off-line retrieval. Besides if an accident occurs on the blockchain or storage network that is different from the expectation, such as fork, the data state on-chain and off-chain would may be inconsistent. Therefore, we propose the concept of State for P2P storage network to represent the resource allocation of data and assist in the off-line retrieval of resources. We propose a state-aware model based on the permissioned blockchain and P2P storage network, which core is a data structure called StateSnap. The model supports the verification of the consistency of the on-chain and off-chain data and support State rollback and switching. Through experiments, we show that our model reduces the data retrieval time by about 78\(\%\) compared to the traditional IPFS and performs well in terms of scalability, robustness, and State switch efficiency.
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Acknowledgement
This work is supported by the National Natural Science Foundation, China (No.61772316); the major Science and Technology Innovation of Shandong Province (No. 2019JZZY010109, 2020CXGC010106); the Industrial Experts Program of Spring City; National Social Science Fund (No. 20BJY131); the Special Project of Science and Technology Innovation Base of Key Laboratory of Shandong Province for Software Engineering (Project ID:11480004042015).
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Feng, S., Li, W., Kong, L., Liu, L., Jin, F., Min, X. (2022). StateSnap: A State-Aware P2P Storage Network for Blockchain NFT Content Data. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_1
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