Data-Availability-Aware Hybrid Storage Optimization in Permissioned Blockchains: A Multi-Objective Metaheuristic Approach

Abstract

Modern permissioned blockchain systems increasingly adopt hybrid data architectures in which critical metadata are anchored on-chain, while large or sensitive payloads are stored off-chain using infrastructures such as IPFS and cloud services. Although this paradigm improves scalability and cost efficiency, it introduces a coupled design challenge where latency, operational cost, and security must be balanced simultaneously. Existing Layer-2 and data-availability approaches primarily focus on throughput and verification, leaving data placement decisions in enterprise permissioned environments insufficiently explored. This paper formulates hybrid on-chain, IPFS, and cloud data placement as a multi-objective optimization problem that jointly encodes storage location, transaction execution mode, and key blockchain parameters, aiming to minimize latency and cost while maximizing integrity and resilience. To explore this high-dimensional design space without costly physical deployment, a digital-twin-based evaluation framework is proposed to approximate the performance, cost, and security behavior of a Fabric-class permissioned blockchain integrated with IPFS and cloud storage. The optimization problem is solved using NSGA-II, yielding a Pareto front that reveals fundamental trade-offs among hybrid configurations. The results demonstrate that hash-anchored off-chain storage consistently outperforms purely on-chain and purely off-chain strategies by reducing latency and cost while preserving strong integrity and replication guarantees. The proposed framework provides practical decision support for data-availability-aware permissioned blockchains in domains such as supply chains, healthcare, and disaster response.