FlexChain: An Elastic Disaggregated Blockchain
FlexChain: An Elastic Disaggregated Blockchain
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DOI：
10.14778/3561261.3561264
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Chenyuan Wu;Mohammad Javad Amiri;Heena Nagda;Qizhen Zhang;B. T. Loo
中科院分区：
文献类型：
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作者：
Chenyuan Wu;Mohammad Javad Amiri;Heena Nagda;Qizhen Zhang;B. T. Loo
虽然许可的区块链支持一系列数据中心应用程序，但现有系统在计算和内存之间的负载不平衡，加剧了云资源的未充分利用。本文提出了FlexChain，这是一种新型的授权区块链系统，通过物理分解CPU、DRAM和存储设备来有效处理不同的区块链工作负载，从而解决这一挑战。解聚允许区块链服务提供商独立升级和扩展硬件资源，以支持具有不同CPU和内存需求的广泛智能合同。此外，它还可确保高效的资源利用，从而防止数据中心中的资源碎片化。我们以分解的方式探索了XOV区块链系统的设计，并开发了可以弹性扩展其内存和存储的分层键值存储。我们的设计大大加快了执行阶段。我们还利用了几种技术来并行FlexChain中的验证阶段，以进一步提高区块链的整体性能。我们的评估结果表明，FlexChain可以提供独立的计算和内存可伸缩性，而解聚开销最高可达12.8%。对于计算密集型和内存密集型工作负载，FlexChain实现了与最先进的分布式方法几乎相同的吞吐量，并显著降低了内存和CPU消耗。
While permissioned blockchains enable a family of data center applications, existing systems suffer from imbalanced loads across compute and memory, exacerbating the underutilization of cloud resources. This paper presents FlexChain , a novel permissioned blockchain system that addresses this challenge by physically disaggregating CPUs, DRAM, and storage devices to process different blockchain workloads efficiently. Disaggregation allows blockchain service providers to upgrade and expand hardware resources independently to support a wide range of smart contracts with diverse CPU and memory demands. Moreover, it ensures efficient resource utilization and hence prevents resource fragmentation in a data center. We have explored the design of XOV blockchain systems in a disaggregated fashion and developed a tiered key-value store that can elastically scale its memory and storage. Our design significantly speeds up the execution stage. We have also leveraged several techniques to parallelize the validation stage in FlexChain to further improve the overall blockchain performance. Our evaluation results show that FlexChain can provide independent compute and memory scalability, while incurring at most 12.8% disaggregation overhead. FlexChain achieves almost identical throughput as the state-of-the-art distributed approaches with significantly lower memory and CPU consumption for compute-intensive and memory-intensive workloads respectively.