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Dynamic Mining Interval to Improve Blockchain Throughput (2312.14038v1)

Published 21 Dec 2023 in cs.CR

Abstract: Decentralized Finance (DeFi), propelled by Blockchain technology, has revolutionized traditional financial systems, improving transparency, reducing costs, and fostering financial inclusion. However, transaction activities in these systems fluctuate significantly and the throughput can be effected. To address this issue, we propose a Dynamic Mining Interval (DMI) mechanism that adjusts mining intervals in response to block size and trading volume to enhance the transaction throughput of Blockchain platforms. Besides, in the context of public Blockchains such as Bitcoin, Ethereum, and Litecoin, a shift towards transaction fees dominance over coin-based rewards is projected in near future. As a result, the ecosystem continues to face threats from deviant mining activities such as Undercutting Attacks, Selfish Mining, and Pool Hopping, among others. In recent years, Dynamic Transaction Storage (DTS) strategies were proposed to allocate transactions dynamically based on fees thereby stabilizing block incentives. However, DTS' utilization of Merkle tree leaf nodes can reduce system throughput. To alleviate this problem, in this paper, we propose an approach for combining DMI and DTS. Besides, we also discuss the DMI selection mechanism for adjusting mining intervals based on various factors.

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References (14)
  1. D. Easley, M. O’Hara, and S. Basu, “From Mining to Markets: The Evolution of Bitcoin Transaction Fees,” Journal of Financial Economics, vol. 134, no. 1, pp. 91–109, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0304405X19300583
  2. M. Carlsten, H. Kalodner, S. M. Weinberg, and A. Narayanan, “On the instability of bitcoin without the block reward,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, ser. CCS ’16.   New York, NY, USA: Association for Computing Machinery, 2016, p. 154–167. [Online]. Available: https://doi.org/10.1145/2976749.2978408
  3. I. Eyal and E. G. Sirer, “Majority is not enough: Bitcoin mining is vulnerable,” 2013.
  4. M. Rosenfeld, “Analysis of bitcoin pooled mining reward systems,” 2011.
  5. X. Zhao and Y.-W. Si, “Dynamic transaction storage strategies for a sustainable blockchain,” 2022.
  6. M. Carlsten, H. Kalodner, S. M. Weinberg, and A. Narayanan, “On the instability of bitcoin without the block reward,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016, pp. 154–167.
  7. N. Singh and M. Vardhan, “Multi-objective optimization of block size based on cpu power and network bandwidth for blockchain applications,” 2020.
  8. Y. Shahsavari, K. Zhang, and C. Talhi, “A theoretical model for fork analysis in the bitcoin network,” in 2019 IEEE International Conference on Blockchain (Blockchain), 2019, pp. 237–244.
  9. D. Kraft, “Difficulty control for blockchain-based consensus systems,” Peer-to-Peer Networking and Applications, vol. 9, pp. 397–413, 2016.
  10. W. Feng, Z. Cao, J. Shen, and X. Dong, “Rtpow: A proof-of-work consensus scheme with real-time difficulty adjustment algorithm,” in 2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS), 2021, pp. 233–240.
  11. M. Cao, H. Wang, T. Yuan, K. Xu, K. Lei, and J. Wang, “Meta-regulation: Adaptive adjustment to block size and creation interval for blockchain systems,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 12, pp. 3702–3718, 2022.
  12. R. Kanda and K. Shudo, “Block interval adjustment toward fair proof-of-work blockchains,” in 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW), 2020, pp. 1–6.
  13. C. Decker and R. Wattenhofer, “Information propagation in the bitcoin network,” in 13th IEEE International Conference on Peer-to-Peer Computing, IEEE P2P 2013, Trento, Italy, September 9-11, 2013, Proceedings.   IEEE, 2013, pp. 1–10. [Online]. Available: https://doi.org/10.1109/P2P.2013.6688704
  14. Kaggle.com, “Bitcoin Historical Data,” https://www.kaggle.com/mczielinski/bitcoin-historical-data, Last accessed: 20 Jan 2021. [Online]. Available: https://www.kaggle.com/mczielinski/bitcoin-historical-data

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