  Similar to other transaction processing frameworks, blockchain systems need
to be dynamically reconfigured to adapt to varying workloads and changes in
network conditions. However, achieving optimal reconfiguration is particularly
challenging due to the complexity of the blockchain stack, which has diverse
configurable parameters. This paper explores the concept of self-driving
blockchains, which have the potential to predict workload changes and
reconfigure themselves for optimal performance without human intervention. We
compare and contrast our discussions with existing research on databases and
highlight aspects unique to blockchains. We identify specific parameters and
components in Hyperledger Fabric, a popular permissioned blockchain system,
that are suitable for autonomous adaptation and offer potential solutions for
the challenges involved. Further, we implement three demonstrative locally
autonomous systems, each targeting a different layer of the blockchain stack,
and conduct experiments to understand the feasibility of our findings. Our
experiments indicate up to 11% improvement in success throughput and a 30%
decrease in latency, making this a significant step towards implementing a
fully autonomous blockchain system in the future.
