Dependency Reduction Techniques for Performance Improvement of Hyperledger Fabric Blockchain

We propose dependency reduction techniques for the performance enhancement of the Hyperledger Fabric blockchain. A dependency hazard may result from the parallelism in Hyperledger Fabric, which executes multiple transactions simultaneously in a single block. Since multiple transactions in a block are executed in parallel for throughput enhancement, dependency problems may arise among transactions involving the same key (If Z = A + D is executed in parallel with A = B + C, a read-after-write hazard for A will occur). To address these issues, our scheme proposes a transaction dependency checking system that integrates a dependency-tree-based management approach to dynamically prioritize transactions based on factors such as the tree level, arrival time, and starvation possibility. Our scheme constructs a dependency tree for transactions in a block to be executed in parallel over multiple execution units. We rearrange the transactions into blocks in such a way that the dependency among the transactions are removed as far as possible. This allows parallel execution of transactions to be performed without collision, enhancing the throughput against the conventional implementation of Hyperledger Fabric. Our illustrative implementation of the proposed scheme in a testbed for trading renewable energy shows a performance improvement as big as 27%, depending on the input mixture of transactions. A key innovation is the introduction of the Starve-Avoid method, which mitigates data starvation by dynamically adjusting the transaction priorities to balance throughput and fairness, ensuring that no transaction experiences indefinite delays. Unlike existing approaches that require structural modifications to the conventional Hyperledger Fabric, the proposed scheme optimizes the performance as an independent module, maintaining compatibility with the conventional Hyperledger Fabric architecture.