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Optimized Execution of Business Processes on Blockchain

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10445))

Abstract

Blockchain technology enables the execution of collaborative business processes involving untrusted parties without requiring a central authority. Specifically, a process model comprising tasks performed by multiple parties can be coordinated via smart contracts operating on the blockchain. The consensus mechanism governing the blockchain thereby guarantees that the process model is followed by each party. However, the cost required for blockchain use is highly dependent on the volume of data recorded and the frequency of data updates by smart contracts. This paper proposes an optimized method for executing business processes on top of commodity blockchain technology. Our optimization targets three areas specifically: initialization cost for process instances, task execution cost by means of a space-optimized data structure, and improved runtime components for maximized throughput. The method is empirically compared to a previously proposed baseline by replaying execution logs and measuring resource consumption and throughput.

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Notes

  1. 1.

    https://www.ethereum.org/ – last accessed 4/3/2017.

  2. 2.

    https://github.com/ethereum/EIPs/issues/93 – last accessed 20/3/2017.

  3. 3.

    The transformation cannot handle escalation and signal events and non-interrupting boundary events, but these constructs are beyond the scope of this paper.

  4. 4.

    http://www.minitlabs.com/ – last accessed 13/3/2017.

  5. 5.

    https://github.com/ethereum/go-ethereum/wiki/geth – last accessed 20/3/2017.

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Acknowledgements

This research was started at the Dagstuhl seminar #16191 – Fresh Approaches to Business Process Modeling. The research is partly supported by the Estonian Research Council (grant IUT20-55).

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Correspondence to Luciano García-Bañuelos .

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García-Bañuelos, L., Ponomarev, A., Dumas, M., Weber, I. (2017). Optimized Execution of Business Processes on Blockchain. In: Carmona, J., Engels, G., Kumar, A. (eds) Business Process Management. BPM 2017. Lecture Notes in Computer Science(), vol 10445. Springer, Cham. https://doi.org/10.1007/978-3-319-65000-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-65000-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64999-3

  • Online ISBN: 978-3-319-65000-5

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