Blockchain Goes Green? Part II: Characterizing the Performance and Cost of Blockchains on the Cloud and at the Edge
School of Computing, National University of Singapore
arXiv:2205.06941 [cs.DC], (14 May 2022)
@misc{https://doi.org/10.48550/arxiv.2205.06941,
doi={10.48550/ARXIV.2205.06941},
url={https://arxiv.org/abs/2205.06941},
author={Loghin, Dumitrel and Dinh, Tien Tuan Anh and Maw, Aung and Gang, Chen and Teo, Yong Meng and Ooi, Beng Chin},
keywords={Distributed, Parallel, and Cluster Computing (cs.DC), Databases (cs.DB), Performance (cs.PF), FOS: Computer and information sciences, FOS: Computer and information sciences},
title={Blockchain Goes Green? Part II: Characterizing the Performance and Cost of Blockchains on the Cloud and at the Edge},
publisher={arXiv},
year={2022},
copyright={Creative Commons Attribution 4.0 International}
}
While state-of-the-art permissioned blockchains can achieve thousands of transactions per second on commodity hardware with x86/64 architecture, their performance when running on different architectures is not clear. The goal of this work is to characterize the performance and cost of permissioned blockchains on different hardware systems, which is important as diverse application domains are adopting t. To this end, we conduct extensive cost and performance evaluation of two permissioned blockchains, namely Hyperledger Fabric and ConsenSys Quorum, on five different types of hardware covering both x86/64 and ARM architecture, as well as, both cloud and edge computing. The hardware nodes include servers with Intel Xeon CPU, servers with ARM-based Amazon Graviton CPU, and edge devices with ARM-based CPU. Our results reveal a diverse profile of the two blockchains across different settings, demonstrating the impact of hardware choices on the overall performance and cost. We find that Graviton servers outperform Xeon servers in many settings, due to their powerful CPU and high memory bandwidth. Edge devices with ARM architecture, on the other hand, exhibit low performance. When comparing the cloud with the edge, we show that the cost of the latter is much smaller in the long run if manpower cost is not considered.
May 22, 2022 by hgpu
