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Performance Improvement of Distributed Consensus Algorithms for Blockchain through Suggestion and Analysis of Assessment Items

평가항목 제안 및 분석을 통한 블록체인 분산합의 알고리즘 성능 개선

  • Kim, Do Gyun (Department of Industrial Engineering, Ajou University) ;
  • Choi, Jin Young (Department of Industrial Engineering, Ajou University) ;
  • Kim, Kiyoung (Electronics and Telecommunications Research Institute) ;
  • Oh, Jintae (Electronics and Telecommunications Research Institute)
  • Received : 2018.11.12
  • Accepted : 2018.12.13
  • Published : 2018.12.31

Abstract

Recently, blockchain technology has been recognized as one of the most important issues for the 4th Industrial Revolution which can be represented by Artificial Intelligence and Internet of Things. Cryptocurrency, named Bitcoin, was the first successful implementation of blockchain, and it triggered the emergence of various cryptocurrencies. In addition, blockchain technology has been applied to various applications such as finance, healthcare, manufacturing, logistics as well as public services. Distributed consensus algorithm is an essential component in blockchain, and it enables all nodes belonging to blockchain network to make an agreement, which means all nodes have the same information. For example, Bitcoin uses a consensus algorithm called Proof-of-Work (PoW) that gives possession of block generation based on the computational volume committed by nodes. However, energy consumption for block generation in PoW has drastically increased due to the growth of computational performance to prove the possession of block. Although many other distributed consensus algorithms including Proof-of-Stake are suggested, they have their own advantages and limitations, and new research works should be proposed to overcome these limitations. For doing this, above all things, we need to establish an evaluation method existing distributed consensus algorithms. Based on this motivation, in this work, we suggest and analyze assessment items by classifying them as efficiency and safety perspectives for investigating existing distributed consensus algorithms. Furthermore, we suggest new assessment criteria and their implementation methods, which can be used for a baseline for improving performance of existing distributed consensus algorithms and designing new consensus algorithm in future.

Keywords

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