Skip to main content
Springer Nature Link
Log in

Scalable blockchain storage mechanism based on two-layer structure and improved distributed consensus

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Existing public blockchain architectures suffer from the difficulty of scaling to support large-scale networks with high TPS, low latency and security. Using the idea of network fragmentation, an improved Raft-based PBFT consensus mechanism is proposed to solve the problem. The network nodes are grouped, and the group adopts the improved Raft mechanism for consensus, and then, the leaders elected in each group form the network committee, and the network committee uses the PBFT mechanism for consensus within the network committee. The results show that R-PBFT is more scalable than PBFT and Raft in a large-scale network environment because it can guarantee high consensus efficiency while having Byzantine fault tolerance; similarly, to improve the fairness between user experience and TPS in blockchain systems, based on the transaction fairness model, a fairness packing algorithm is proposed for storing transactions. It is based on a two-level model, firstly sorting the transactions in descending order based on GasPrice, moreover considering the fairness model for descending order. The experimental confirmed that all the performance of fairness packing is superior to Ethereum packing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Berdik D, Otoum S, Schmidt N et al (2021) A survey on blockchain for information systems management and security. Inf Process Manag 58(1):102397

    Article  Google Scholar 

  2. Li Y, Zheng K, Yan Y et al (2017) EtherQL: a query layer for Blockchain System[M]. In: Candan S, Chen L, Pedersen TB, Chang L, Hua W (eds) Database systems for advanced applications. Springer, Cham

    Google Scholar 

  3. Brewer EA (2000) Towards robust distributed systems. In: Proceedings of the nineteenth annual ACM symposium on principles of distributed computing. ACM Press, New York, 343502. https://doi.org/10.1145/343477

  4. Wang WB, Dinh TH, Xiong ZH et al. (2018) A survey on consensus mechanisms and mining management in blockchain networks. ar-Xiv Preprint, arXiv:1805.02707

  5. Jain RK, Chiu DMW, Hawe WR (1984) A quantitative measure of fairness and discrimination. Eastern Research Laboratory, Digital Equipment Corporation, Hudson, MA

  6. Yuan Y, Wang FY (2016) Blockchain: the state of the art and future trends. Acta Autom Sin 42(4):481–494

    Google Scholar 

  7. Nakamoto S (2019) Bitcoin: a peer-to-peer electronic cash System. consulted

  8. Wood G (2014) Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj yellow Pap 2014 (151):1–32

    Google Scholar 

  9. Dinh TTA, Wang J, Chen G, Liu R, Ooi BC, Tan KL (2017) Blockbench: a framework for analyzing private blockchains. In: Proceedings of the 2017 ACM international conference on management of data, pp 1085-1100

  10. Kotewicz M (2021) Parity [EB/OL]. https://github.com/openethereum/parity-ethereum. Accessed 29 Mar

  11. Thakkar P, Nathan S, Viswanathan B (2018) Performance benchmarking and optimizing hyperledger fabric blockchain platform. In: IEEE 26th international symposium on modeling, analysis, and simulation of computer and telecommunication systems (MASCOTS). IEEE 8: 264–276

  12. Gorenflo C, Lee S, Golab L, Keshav S (2020) Fastfabric: scaling hyperledger fabric to 20 000 trans- actions per second. Int J Netw Manag 30(5):e2099

    Article  Google Scholar 

  13. Pongnumkul S, Siripanpornchana C, Thajchayapong S (2017) Performance analysis of private blockchain platforms in varying workloads. In: 26th International conference on computer communication and networks (ICCCN). IEEE, pp 1–6

  14. Rouhani S, Deters R (2017) Performance analysis of ethereum transactions in private blockchain. In: 8th IEEE international conference on software engineering and service science (ICSESS). IEEE, pp 70–74

  15. Li C, Bai J, Yi C et al (2020) Resource and replica management strategy for optimizing financial cost and user experience in edge cloud computing system. Inf Sci 516:33–55

    Article  MathSciNet  Google Scholar 

  16. Li C, Song M, Zhang M, Luo Y (2020) Effective replica management for improving reliability and availability in edge-cloud computing environment. J Parallel Distrib Comput 143:107–128

    Article  Google Scholar 

  17. Ampel B, Patton M, Chen H (2019) Performance modeling of hyperledger sawtooth blockchain. In: IEEE international conference on intelligence and security informatics (ISI). IEEE, pp 59–61

  18. Hao Y, Li Y, Dong X, Fang L, Chen P (2018) Performance analysis of consensus algorithm in private blockchain. In: IEEE intelligent vehicles symposium (IV). IEEE, pp 280–285

  19. Suankaewmanee K, Hoang DT, Niyato D, Sawadsitang S, Wang P, Han Z (2018) Performance analysis and application of mobile blockchain. In: International conference on computing, networking and communications (ICNC). IEEE, pp 642–646

  20. Chen S, Zhang J, Shi R, Yan J, Ke Q (2018) A comparative testing on performance of blockchain and relational database: Foundation for applying smart technology into current business systems. In: International conference on distributed, ambient, and pervasive interactions. Springer, pp 21–34

  21. Baliga A, Subhod I, Kamat P, Chatterjee S (2018) Performance evaluation of the quorum blockchain platform. arXiv preprint arXiv:1809.03421

  22. Huang D, Ma X, Zhang S (2019) Performance analysis of the raft consensus algorithm for private blockchains. IEEE Transact Syst Man Cybern Syst 50(1):172–181

    Article  Google Scholar 

  23. Gervais A, Karame GO, Wüst K, Glykantzis V, Ritzdorf H, Capkun S (2016) On the security and performance of proof of work blockchains. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp 3-16

  24. van Moorsel A (2019) Benchmarks and models for blockchain: consensus algorithms. ACM SIGMETRICS Perform Eval Rev 46(3):113–113

    Article  Google Scholar 

  25. Qashlan A, Nanda P, He X (2020) Automated ethereum smart contract for block chain based smart home security. In: smart systems and IoT: innovations in computing. Springer, Singapore, pp 313–326

    Google Scholar 

  26. Li C, Zhang Y, Zhiqiang H et al (2020) An effective scheduling strategy based on hypergraph partition in geographically distributed datacenters. Computer Netw 170:107096

    Article  Google Scholar 

  27. Li C, Tang J, Ma T, Yang X, Luo Y (2020) A workflow job scheduling algorithm based on load balancing in distributed cloud. J Netw Comput Appl 152:102518

    Article  Google Scholar 

  28. Cha SC, Peng WC, Huang ZJ et al (2017) On design and implementation a smart contract-based investigation report management framework for smartphone applications. In: International conference on intelligent information hiding and multimedia signal processing, Springer, Cham, pp 282–289

  29. Knecht M, Stiller B (2017) Smartdemap: A smart contract deployment and management platform. In: IFIP international conference on autonomous infrastructure, management and security. Springer, Cham 159–164

  30. Li A, Wei X, He Z (2020) Robust proof of stake: a new consensus protocol for sustainable blockchain systems. Sustainability 12:2824

    Article  Google Scholar 

  31. Karakostas D, Kiayias A (2021) Securing proof-of-work ledgers via checkpointing. In: IEEE international conference on blockchain and cryptocurrency (ICBC), pp 1-5. https://doi.org/10.1109/ICBC51069.2021.9461066

  32. Howard H, Mortier R (2020) Paxos vs Raft: Have we reached consensus on distributed consensus?. In: Proceedings of the 7th workshop on principles and practice of consistency for distributed data, pp 1–9

  33. Castro M, Liskov B (1999) Practical byzantine fault tolerance. In: Proceedings of the third symposium on operating systems design and implementation. ACM Press, New York, pp 173–186

  34. Lamport L (1998) The part-time parliament. ACM Trans Comput Syst 16(2):133–169

    Article  Google Scholar 

  35. Ongaro D, Ousterhou TJ (2014) In search of an understandable consensus algorithm. In: Proceedings of USENIX ATC’14: 2014 USENIX annual technical conference. USENIX association, Berkeley, pp 305–320

  36. Eyal I, Gencer AE, Sirer EG et al (2016) Bitcoin-ng: A scalable blockchain protocol. In: 13th {USENIX} symposium on networked systems design and implementation ({NSDI} 16), pp 45–59

  37. Kogias EK, Jovanovic P, Gailly N, et al. (2016) Enhancing bitcoin security and performance with strong consistency via collective signing. In: 25th {usenix} security symposium ({usenix} security 16), pp 279–296

  38. Eyal I, Birman K, Van Renesse R (2015) Cache serializability: reducing inconsistency in edge transactions. In: 2015 IEEE 35th international conference on distributed computing systems. IEEE, pp 686–695

  39. Buterin V (2014) A next-generation smart con-tract and decentralized application platform. Etherum 1:1–36

    Google Scholar 

  40. Larimer D (2014) Delegated proof of stake consensus[R]. [2020–08–19]

  41. Xu X, Sun G, Luo L et al (2021) Latency performance modeling and analysis for hyperledger fabric blockchain network. Inf Process Manag 58(1):102436

    Article  Google Scholar 

  42. Castro M, Liskov B (1999) Practical Byzantine fault tolerance. In: Proc Symp Oper Syst Design Implement, New Orleans, LA, USA, pp 173–186

  43. Lamport L (1998) The part-time parliament. ACM Trans Comput Syst 16(2):133–169

    Article  Google Scholar 

  44. Moraru I, Andersen DG, Kaminsky M (2013) There is more consensus in egalitarian parliaments. In: Proc ACM Symp Oper Syst Principles (SOSP), Farmington, PA, USA 358–372

  45. Reyna A et al (2018) On blockchain and its integration with IoT. Challenges and opportunities. Future Gener Comput Syst 88:173–190

    Article  Google Scholar 

  46. Liang W et al (2020) An industrial network intrusion detection algorithm based on multi-feature data clustering optimization model. IEEE Trans Ind Informat 16(3):2063–2071. https://doi.org/10.1109/TII.2019.2946791

    Article  Google Scholar 

  47. Rizun PR (2015) A transaction fee market exists without a block size limit. Block size limit debate working paper

  48. Li C, Song M, Yu C, Luo Y (2021) Mobility and marginal gain based content caching and placement for cooperative edge-cloud computing. Inf Sci 548:153–176

    Article  Google Scholar 

  49. Möser M, Böhme R (2015) Trends, tips, tolls: a longitudinal study of Bitcoin transaction fees. In: International conference on financial cryptography and data security. Springer, Berlin, Heidelberg, pp 19–33

Download references

Acknowledgements

The work was supported by the National Natural Science Foundation (NSF) under grants (No. 61873341, 62171330), Key Research and Development Plan of Hubei Province (No. 2020BAB102), Open project of CAAC Key Laboratory of Civil Aviation Wide Surveillance and Safety Operation Management & Control Technology (No.202001), Open Research Fund Program of Data Recovery Key Laboratory of Sichuan Province (Grant No. DRX2001). Any opinions, findings and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

Author information

Authors and Affiliations

  1. CAAC Key Laboratory of Civil Aviation Wide Survellence and Safety Operation Management & Control Technology, Tianjin, China

    Chunlin Li

  2. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430063, China

    Chunlin Li & Jing Zhang

  3. Data Recovery Key Laboratory of Sichuan Province, College of Mathematics and Information Science, Neijiang Normal University, Neijiang, 641100, People’s Republic of China

    Xianmin Yang

Authors
  1. Chunlin Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jing Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xianmin Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Chunlin Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, C., Zhang, J. & Yang, X. Scalable blockchain storage mechanism based on two-layer structure and improved distributed consensus. J Supercomput 78, 4850–4881 (2022). https://doi.org/10.1007/s11227-021-04061-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-04061-3

Keywords