Performance Analysis of Dynamic Scaling in Hyperledger Fabric-Based Academic Credential Verification Systems
Abstract
Fraudulent academic credentials pose a growing challenge for institutions worldwide. Permissioned blockchains offer a promising infrastructure for tamper-resistant credential verification, yet no prior study has evaluated multiple auto-scaling strategies for such systems under realistic, variable workloads. This paper addresses that gap by evaluating four auto-scaling configurations for a Hyperledger Fabric network deployed on Kubernetes and benchmarked using Hyperledger Caliper, testing seven workload scenarios with 50 to 2,000 concurrent users. With an aggressive scaling policy, throughput rose by 115% over a fixed-replica baseline, while latency fell by 67% under heavy load. Even at 1,000 concurrent users, response times stayed below 500 ms at the 99th percentile, and the system absorbed sudden traffic spikes within about 50 seconds. However, aggressive scaling consumed more cluster resources during low-demand periods. These findings provide a practical decision guide for selecting scaling configurations that balance responsiveness against resource efficiency in permissioned blockchain deployments.
Keywords
Ethical Statement
References
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Details
Primary Language
English
Subjects
Data and Information Privacy, Computer Software
Journal Section
Research Article
Authors
Sefa Tunçer
*
0000-0001-6672-3605
Türkiye
Publication Date
September 30, 2026
Submission Date
February 7, 2026
Acceptance Date
June 18, 2026
Published in Issue
Year 2026 Volume: 9 Number: 4