Abstract
The cost of trust in the real economy is obviously reduced by the technology of blockchain, however, storage scalability of blockchain is quite poor. Therefore, the ElasticChain model is took use of to solve this problem. The ElasticChain model increases the storage capacity of blockchain, while security of data is the premise which we must guarantee. In ElasticChain, the duplicate ratio regulation algorithm makes two things happen. The first one is to give a safe distribution of duplicates and then the other thing is the duplicates are stored in highly reliable nodes. However, in ElasticChain, the reliability test method of storage nodes is too simple. Therefore, in order to solve this problem, it is necessary for us to put forward an optimized data distribution method for ElasticChain. The optimized model ought be able to use the Extreme Learning Machine (ELM) to classify the nodes because of their different reliability. Moreover, the optimized model can also distribute the blockchain data in reliable nodes, so as to increase data security. Further, we brought forward a new strategy to extract the node reliability. The fact is that the node reliability includes the features below: the activeness, security, credibility and stability. There are four features in total. In this substance, the efficiency and exactness of the optimized data distribution model are certificated by the results of experiments on synthetic data.
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Acknowledgments
This research was partially supported by the National Natural Science Foundation of China (Nos. 61472069, 61402089, 61402298 and U1401256), the Fundamental Research Funds for the Central Universities (Nos. N161602003, N171607010, N161904001, and N160601001), the Natural Science Foundation of Liaoning Province (Nos. 2015020553 and 20170540702).
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Jia, D., Xin, J., Wang, Z., Guo, W., Wang, G. (2020). An Optimized Data Distribution Model for ElasticChain to Support Blockchain Scalable Storage. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_9
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DOI: https://doi.org/10.1007/978-3-030-23307-5_9
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