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arXiv · 2003.05113

Scaling Hyperledger Fabric Using Pipelined Execution and Sparse Peers

Parth Thakkar·Senthilnathan Natarajan
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Abstract

Permissioned blockchains are becoming popular as data management systems in the enterprise setting. Compared to traditional distributed databases, blockchain platforms provide increased security guarantees but significantly lower performance. Further, these platforms are quite expensive to run for the low throughput they provide. The following are two ways to improve performance and reduce cost: (1) make the system utilize allocated resources efficiently; (2) allow rapid and dynamic scaling of allocated resources based on load. We explore both of these in this work. We first investigate the reasons for the poor performance and scalability of the dominant permissioned blockchain flavor called Execute-Order-Validate (EOV). We do this by studying the scaling characteristics of Hyperledger Fabric, a popular EOV platform, using vertical scaling and horizontal scaling. We find that the transaction throughput scales very poorly with these techniques. At least in the permissioned setting, the real bottleneck is transaction processing, not the consensus protocol. With vertical scaling, the allocated vCPUs go under-utilized. In contrast, with horizontal scaling, the allocated resources get wasted due to redundant work across nodes within an organization. To mitigate the above concerns, we first improve resource efficiency by (a) improving CPU utilization with a pipelined execution of validation & commit phases; (b) avoiding redundant work across nodes by introducing a new type of peer node called sparse peer that selectively commits transactions. We further propose a technique that enables the rapid scaling of resources. Our implementation - SmartFabric, built on top of Hyperledger Fabric demonstrates 3x higher throughput, 12-26x faster scale-up time, and provides Fabric's throughput at 50% to 87% lower cost.

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Parth Thakkar, Senthilnathan Natarajan. 2020-03-11. Scaling Hyperledger Fabric Using Pipelined Execution and Sparse Peers. https://arxiv.org/abs/2003.05113

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