Scalability Analysis of Blockchain on a Serverless Cloud

• Published in: 2019 IEEE International Conference on Big Data (Big Data) pp. 4214 - 4222
• Main Authors: Kaplunovich, Alex, Joshi, Karuna P., Yesha, Yelena
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2019
• Subjects: automation; AWS Cloud; benchmark; Benchmark testing; blockchain; Cloud computing; Fabrics; Lambda; messaging; Scalability; serverless; Servers; Time series analysis
• DOI: 10.1109/BigData47090.2019.9005529

While adopting Blockchain technologies to automate their enterprise functionality, organizations are recognizing the challenges of scalability and manual configuration that the state of art present. Scalability of Hyperledger Fabric is an open challenge recognized by the research community. We have automated many of the configuration steps of installing Hyperledger Fabric Blockchain on AWS infrastructure and have benchmarked the scalability of that system. We have used the UCR (University of California Riverside) Time Series Archive with 128 timeseries datasets containing over 191,177 rows of data totaling 76,453,742 numbers. Using an automated Serverless approach, we have loaded this dataset, by chunks, into different AWS instances, triggering the load by SQS messaging. In this paper, we present the results of this benchmarking study and describe the approach we took to automate the Hyperledger Fabric processes using serverless Lambda functions and SQS triggering. We will also discuss what is needed to make the Blockchain technology more robust and scalable.