Distributed load-balancing for account-based sharded blockchains

Sharding of blockchains consists of partitioning a blockchain network into several sub-networks called “shards,” each shard processing and storing disjoint sets of transactions in parallel. Sharding has recently been applied to public blockchains to improve scalability through parallelism. The throughput of sharded blockchain is optimized when the workload among the shards is approximately the same. The purpose of this paper is to investigate the problem of balancing workload of account-based blockchains such as Ethereum.

Two known consensus-based distributed load-balancing algorithms have been adapted to sharded blockchains. These algorithms migrate accounts across shards to balance transaction processing times. Two methods to predict transaction processing times are proposed.

The authors identify some challenging aspects for solving the load-balancing problem in sharded blockchains. Experiments conducted with Ethereum transactions show that the two load-balancing algorithms are challenged by accounts often created to process a single transaction to optimize anonymity, while existing accounts sparsely generate transactions.

Tests in this work have been conducted on transactions originating from a blockchain platform rather than using artificially generated data distributions. They show the specificity of the load-balancing problem for sharded blockchains, which were hidden in artificial data sets.