End-to-End and Phase-Level Performance Optimization for Hyperledger Fabric

A new arXiv paper by Pavan Sollu and colleagues presents a systematic end-to-end and phase-level performance optimization study for Hyperledger Fabric (HLF), the modular permissioned blockchain widely used in enterprise settings. The authors introduce two novel techniques targeting commit-phase bottlenecks: block-level pipelining overlaps validation and private-data acquisition of successive blocks with state-consistency checks and ledger updates, achieving up to 1.9x higher commit throughput. Strategic waiting coordinates commit progress by temporarily pausing fast leaders and boosting laggers to sustain endorsement parallelism, yielding an additional 1.2x throughput improvement. These results were validated through production-grade testbed experiments and calibrated SimPy simulations.

Beyond the core optimizations, the paper conducts micro-benchmarking of three configuration levers: private-data dissemination, block-size selection, and endorsement peer selection. Key findings include: (i) Relaxed quorums for private-data dissemination significantly reduce latency in both endorsement and commit phases; (ii) Under light workloads, smaller blocks yield lower end-to-end latency, while heavy workloads demand larger blocks for throughput and latency; (iii) Relaxed leader selection dramatically reduces dropped transactions and boosts endorsement throughput, albeit with a modest increase in MVCC invalidations. The interplay between private-data dissemination, VSCC parallelization, and pipelined commits shows that throughput gains over a serial commit path peak at moderate parallelization. These phase-aware, protocol-level refinements offer concrete guidance for optimizing HLF deployments in production.