Year: 2023
Type: article
Abstract: This paper presents AdaChain , a learning-based blockchain framework that adaptively chooses the best permissioned blockchain architecture to optimize effective throughput for dynamic transaction workloads. AdaChain addresses the challenge in Blockchain-as-a-Service (BaaS) environments, where a large variety of possible smart contracts are deployed with different workload characteristics. AdaChain supports automatically adapting to an underlying, dynamically changing workload through the use of reinforcement learning. When a promising architecture is identified, AdaChain switches from the current architecture to the promising one at runtime in a secure and correct manner. Experimentally, we show that AdaChain can converge quickly to optimal architectures under changing workloads and significantly outperform fixed architectures in terms of the number of successfully committed transactions, all while incurring low additional overhead.
Authors: Chenyuan Wu, Bhavana Mehta, Mohammad Javad Amiri, Ryan Marcus, Boon Thau Loo
Institution: University of Pennsylvania
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: NeTS: Medium: Collaborative Research: Diagnosing Datacenter Networks with Quantitative Provenance, Collaborative Research: FoMR: Taming the Instruction Bottleneck in Modern Datacenter Applications, CNS Core: Small: Towards Internet-scale Permissioned Blockchain Infrastructure for the Mobile Internet
OpenAlex ID: W4381621957