Adaptive Scalability Optimization for Blockchain-Powered Academic Credential Repositories Using Intelligent Caching and Metadata-Aware Sharding
DOI:
https://doi.org/10.51903/jtie.v5i2.555Keywords:
Academic Credential Verification, Intelligent Caching, Metadata-Aware Sharding, Redis, Scalability Optimization, Distributed Educational RepositoryAbstract
Academic credential verification remains difficult for institutions because manual checks are slow, fragmented, and vulnerable to fraud. Blockchain can improve trust by anchoring credential proofs, but repeated verification requests and growing off-chain repositories can still create performance bottlenecks. This study presents an adaptive blockchain-powered academic credential repository that combines off-chain MySQL storage, Solidity-based hash anchoring, Redis verification caching, and metadata-aware sharding. Full academic records are not stored on-chain or in Redis; only credential hashes, verification responses, and related metadata are used for trust validation and performance optimization. A CodeIgniter 4 prototype was evaluated using synthetic academic credential records and controlled workloads of 1,000, 5,000, and 10,000 verification requests under fresh, mixed, and repeated access patterns. The results show that Redis caching substantially reduced repeated blockchain queries, especially under mixed and repeated workloads, while metadata-aware sharding improved repository organization and supported more targeted credential retrieval. Sepolia testnet validation confirmed smart-contract feasibility, including issuance, verification, revocation, gas use, confirmation time, and event evidence, but was treated separately from scalability testing. The findings indicate that combining blockchain trust anchoring with cache-aware verification and metadata-based repository partitioning can improve the scalability of academic credential repositories, provided that cache consistency, revocation handling, and deployment limitations are carefully managed.
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Data Availability Statement
The experimental data used in this study consist of synthetic, template-informed academic credential records generated for controlled scalability and performance testing; no real student records or production verification logs were used. The data and supporting benchmark configurations are available from the corresponding author upon reasonable request.
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