Performance Improvement by Using Pipelined Execution on Hyperledger Fabric

The rapid growth of proofs of concept blockchain applications leads to increasing interest in understanding and improving blockchain performance at scale. However, the lower performance of blockchain restricts its application in some fields. Our work is focused on evaluating and improving the performance of Hyperledger Fabric, which is the most popular blockchain platform for enterprises. In previous works, the major bottleneck incurred in the validation & commit (V&C) module was studied, and many performance issues arising with it were alleviated to some context. The throughput is still only 900 transactions/second in our experiment. In this paper, a comprehensive latency evaluation for the V&C module was first performed. Then, according to the analysis of the evaluation results, a pipelined execution technology was proposed to process multiple blocks in parallel. Additionally, some pipeline acceleration schemes were also proposed to further improve the performance. Our experiments indicated performance improvements of 4.38× for LevelDB and at least 2× for CouchDB. Notably, our optimizations are transparent to blockchain applications and are suitable for integrating into a future version of Fabric

Comparative analysis of permissioned blockchain frameworks for industrial applications

Blockchain is a technology that creates trust among non-trusting parties, without relying on any intermediary. Consequently, it has attracted the interest of companies operating in a multitude of sectors. However, due to the number of different blockchain solutions that have emerged in the last few years and their rapid changes, it is challenging for such companies to orient their technological decisions. This paper presents a comparative analysis of the key dimensions—namely, governance, maturity, support, latency, privacy, interoperability, flexibility, efficiency, resiliency, and scalability—of some of the most-used permissioned blockchain platforms. Moreover, we present the results of a performance evaluation considering the following frameworks: Hyperledger Fabric 2.2, Hyperledger Sawtooth 1.2, and ConsenSys Quorum 21.1 (with both the GoQuorum client and the Hyperledger Besu client). The platforms were tested under similar conditions, and official releases were used, such that our findings provide a reference for companies establishing their technological orientation

Benchmark and comparison between hyperledger and MySQL

In this paper, we report the benchmarking results of Hyperledger,  a Distributed Ledger, which is the derivation Blockchain Technology.  Method to evaluate Hyperledger in a limited infrastructure is developed. Themeasured infrastructure consists of 8 nodes with a load of up to 20000 transactions/second. Hyperledger consistently runs all evaluation, namely, for 20,000 transactions, the run time 74.30s, latency 73.40ms latency, and 257 tps. The benchmarking of Hyperledger shows better than a database system in a high workload scenario. We found that the maximum size data volume in one transaction on the Hyperledger network is around ten (10) times of MySQL. Also, the time spent on processing a single transaction in the blockchain network is 80-200 times faster than MySQL. This initial analysis can provide an overview for practitioners in making decisions about the adoption of blockchain technology in their IT systems

An In-Depth Investigation of Performance Characteristics of Hyperledger Fabric

Private permissioned blockchains, such as Hyperledger Fabric, are widely deployed across the industry to facilitate cross-organizational processes and promise improved performance compared to their public counterparts. However, the lack of empirical and theoretical results prevent precise prediction of the real-world performance. We address this gap by conducting an in-depth performance analysis of Hyperledger Fabric. The paper presents a detailed compilation of various performance characteristics using an enhanced version of the Distributed Ledger Performance Scan. Researchers and practitioners alike can use the results as guidelines to better configure and implement their blockchains and utilize the DLPS framework to conduct their measurements