Abstract:
Permissioned blockchain technology has a significant impact on many industries due to its security and scalability, which has many configurable knobs to control all aspec...Show MoreMetadata
Abstract:
Permissioned blockchain technology has a significant impact on many industries due to its security and scalability, which has many configurable knobs to control all aspects of it. Too many knobs lead to an exponential increase in the configurable knob combination, so it is vital to find a subset of knobs that affect the performance. This article proposes FIK (Find Important Knobs), which uses (1) Latin hypercube sampling technology to create a data set of knobs and performance. The data set includes more than 10000 samples of three different network architectures of the permissioned blockchain network and three different chaincode. (2) The relationship between knobs and performance is modeled based on the Xgboost method. The performance of this model is better than other selected machine learning models. (3) The SHAP-based method helps decision-makers explain the contribution of the knobs of the permissioned blockchain to performance.
Published in: 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Date of Conference: 04-06 May 2022
Date Added to IEEE Xplore: 20 May 2022
ISBN Information:
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Contents Select All
1.
Z. Zheng, S. Xie, H.-N. Dai, X. Chen, and H. Wang, “Blockchain challenges and opportunities: A survey,” International Journal of Web and Grid Services, vol. 14, no. 4, pp. 352–375, 2018.
2.
E. Cecchetti, F. Zhang, Y. Ji, A. Kosba, A. Juels, and E. Shi, “Solidus: Confidential distributed ledger transactions via pvorm,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 701–717, 2017.
3.
E. Androulaki, A. Barger, V. Bortnikov, C. Cachin, K. Christidis, A. De Caro, D. Enyeart, C. Ferris, G. Laventman, Y. Manevich, et al, “Hyperledger fabric: a distributed operating system for permissioned blockchains,” in Proceedings of the thirteenth EuroSys conference, pp. 1–15, 2018.
4.
A. Sharma, F. M. Schuhknecht, D. Agrawal, and J. Dittrich, “Blurring the lines between blockchains and database systems: the case of hyper-ledger fabric,” in Proceedings of the 2019 International Conference on Management of Data, pp. 105–122, 2019.
5.
S. Hua, S. Zhang, B. Pi, J. Sun, K. Yamashita, and Y. Nomura, “Reasonableness discussion and analysis for hyperledger fabric configuration,” in 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pp. 1–3, IEEE, 2020.
6.
K. Kanellis, R. Alagappan, and S. Venkataraman, “Too many knobs to tune? towards faster database tuning by pre-selecting important knobs,” in 12th {USENIX} Workshop on Hot Topics in Storage and File Systems (HotStorage 20), 2020.
7.
D. Van Aken, A. Pavlo, G. J. Gordon, and B. Zhang, “Automatic database management system tuning through large-scale machine learning,” in Proceedings of the 2017 ACM International Conference on Management of Data, pp. 1009–1024, 2017.
8.
M. D. McKay, R. J. Beckman, and W. J. Conover, “A comparison of three methods for selecting values of input variables in the analysis of output from a computer code,” Technometrics, vol. 42, no. 1, pp. 55–61, 2000.
9.
J. Benesty, J. Chen, Y. Huang, and I. Cohen, “Pearson correlation coefficient,” in Noise reduction in speech processing, pp. 1–4, Springer, 2009.
10.
T. Chen, T. He, M. Benesty, V. Khotilovich, Y. Tang, H. Cho, et al, “Xgboost: extreme gradient boosting,” R package version 0.4-2, vol. 1, no. 4, pp. 1–4, 2015.
11.
S. Hart, “Shapley value,” in Game Theory, pp. 210–216, Springer, 1989.
12.
W. Lyu, Y. Lu, J. Shu, and W. Zhao, “Sapphire: Automatic configuration recommendation for distributed storage systems,” arXiv preprint arXiv:2007.03220, 2020.
13.
L. M. Vaquero and F. Cuadrado, “Auto-tuning distributed stream processing systems using reinforcement learning,” arXiv preprint arXiv:1809.05495, 2018.
14.
Z. Cao, G. Kuenning, and E. Zadok, “Carver: Finding important parameters for storage system tuning,” in 18th {USENIX} Conference on File and Storage Technologies ({FAST} 20), pp. 43–57, 2020.
15.
K. Pearson, “Liii. on lines and planes of closest fit to systems of points in space,” The London, Edinburgh, and Dublin philosophical magazine and journal of science, vol. 2, no. 11, pp. 559–572, 1901.
16.
C. H. Papadimitriou, P. Raghavan, H. Tamaki, and S. Vempala, “Latent semantic indexing: A probabilistic analysis,” Journal of Computer and System Sciences, vol. 61, no. 2, pp. 217–235, 2000.
