• Title/Summary/Keyword: 이레이저 코드

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Research Trends on Distributed Storage Technology for Blockchain Transaction Data (블록체인 트랜잭션 데이터 분산 저장 기술 동향)

  • Choi, B.J.;Kim, C.S.;Lee, M.C.
    • Electronics and Telecommunications Trends
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    • v.37 no.3
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    • pp.85-96
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    • 2022
  • Recently, the blockchain technology, which can decentralize business ecosystems using secure transactions without trusted intermediaries, has been spotlighted. Full nodes play an important role in maintaining decentralization in that they independently verify transactions using their full historical transaction data. However, the storage requirement of a full node for storing historical data is continuously increasing, and thus, has become harder for users to run a full node due to the heavy price for storage costs. In this paper, we investigate research trends on reducing the costs of storing blockchain transaction data so that nodes with low storage requirements can be used in the blockchain network.

Reed-Solomon Encoded Block Storage in Key-value Store-based Blockchain Systems (키값 저장소 기반 블록체인 시스템에서 리드 솔로몬 부호화된 블록 저장)

  • Seong-Hyeon Lee;Jinchun Choi;Myungcheol Lee
    • The Transactions of the Korea Information Processing Society
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    • v.13 no.3
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    • pp.102-110
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    • 2024
  • Blockchain records all transactions issued by users, which are then replicated, stored, and shared by participants of the blockchain network. Therefore, the capacity of the ledger stored by participants continues to increase as the blockchain network operates. In order to address this issue, research is being conducted on methods that enhance storage efficiency while ensuring that valid values are stored in the ledger even in the presence of device failures or malicious participants. One direction of research is applying techniques such as Reed-Solomon encoding to the storage of blockchain ledgers. In this paper, we apply Reed-Solomon encoding to the key-value store used for ledger storage in an open-source blockchain, and measure the storage efficiency and increasing computational overhead. Experimental results confirm that storage efficiency increased by 86% while the increase in CPU operations required for encoding was only about 2.7%.