Elastic Resource Allocation against Imbalanced Transaction Assignments in Sharding-based Permissioned Blockchains

This paper studies the PBFT-based sharded permissioned blockchain, which executes in either a local datacenter or a rented cloud platform. In such permissioned blockchain, the transaction (TX) assignment strategy could be malicious such that the network shards may possibly receive imbalanced transactions or even bursty-TX injection attacks. An imbalanced transaction assignment brings serious threats to the stability of the sharded blockchain. A stable sharded blockchain can ensure that each shard processes the arrived transactions timely. Since the system stability is closely related to the blockchain throughput, how to maintain a stable sharded blockchain becomes a challenge. To depict the transaction processing in each network shard, we adopt the Lyapunov Optimization framework. Exploiting the drift-plus-penalty (DPP) technique, we then propose an adaptive resource-allocation algorithm, which can yield the near-optimal solution for each network shard while the shard queues can also be stably maintained. We also rigorously analyze the theoretical performance boundaries of the proposed DPP algorithm. We particularly evaluate two representative cases of bursty-TX injection attacks. The evaluation results show that the proposed DPP-based algorithm can well alleviate the imbalanced TX assignment, and simultaneously maintain high throughput while consuming fewer resources than other baselines.