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VeriTxn: Verifiable Transactions for Cloud-Native Databases with Storage Disaggregation

Authors:

Beng Chin OoiAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 4

Article No.: 270, Pages 1 - 27

https://doi.org/10.1145/3626764

Published: 12 December 2023 Publication History

Abstract

Cloud-native databases become increasingly popular while exposing to greater data security and correctness risks. Existing verifiable outsourced databases overlook either the correctness risk of transactions, or the disaggregation architecture: a key design consideration of cloud-native databases for performance and elasticity, or both. We present VeriTxn, a novel cloud-native database that efficiently provides verifiability of transaction correctness. VeriTxn relies on the trusted hardware (i.e., Intel SGX) to enable verifiable transaction processing. We build a page-structure cache in the trusted domain, where transactions can be verified with low, constant overhead. VeriTxn further optimizes the read-only transactions by exploiting disaggregation to fit the read-heavy workload in the cloud. We also integrate our proposal into MySQL, a popular open-source database. We conduct extensive experiments to compare VeriTxn against state-of-the-art verifiable databases and evaluate the performance of VeriTxn on MySQL. The results show that VeriTxn introduces tolerable performance degradation for verifiable transactions, while achieving up to 7.03× and 7.93× higher throughput than Litmus and LedgerDB, and its sustainable performance when integrated with MySQL.

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Cited By

Kim TPark CMukimbekov MHong HKim MJin ZKim CShin JJeon M(2024)FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU CooperationProceedings of the VLDB Endowment10.14778/3636218.363623817:4(863-876)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.14778/3636218.3636238
Kim KLee SKim IHan W(2024)ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality EstimationProceedings of the ACM on Management of Data10.1145/36393002:1(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639300
Wang ZWang YDeng JZheng DLi ADing YTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input PreprocessingProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3620665.3640406(964-979)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620665.3640406

Index Terms

VeriTxn: Verifiable Transactions for Cloud-Native Databases with Storage Disaggregation
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database transaction processing
2. Security and privacy
  1. Database and storage security

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 4

PACMMOD

December 2023

1317 pages

EISSN:2836-6573

DOI:10.1145/3637468

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 December 2023

Published in PACMMOD Volume 1, Issue 4

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Cited By

Kim TPark CMukimbekov MHong HKim MJin ZKim CShin JJeon M(2024)FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU CooperationProceedings of the VLDB Endowment10.14778/3636218.363623817:4(863-876)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.14778/3636218.3636238
Kim KLee SKim IHan W(2024)ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality EstimationProceedings of the ACM on Management of Data10.1145/36393002:1(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639300
Wang ZWang YDeng JZheng DLi ADing YTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input PreprocessingProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3620665.3640406(964-979)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620665.3640406

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