Private blockchain networks operate with fixed node configurations that cannot adapt to changing workload conditions. Too many nodes serving a light workload waste resources, while too few nodes facing heavy demand slow down block production and degrade finalization. Determining the right validator count is challenging, as it depends on overlapping factors that shift over time.
This paper presents a Takagi-Sugeno (TS) fuzzy inference system that reads live blockchain parameters (block production time, block size, and active node count) and outputs a continuous efficiency score alongside a scaling recommendation: Scale Up, Maintain, or Scale Down. The controller employs triangular membership functions across three linguistic variables, evaluated through a complete 27-rule base with product t-norm aggregation.
A key contribution is an empirical recalibration of the membership functions, anchoring linguistic terms to the observed operating range of the testbed rather than theoretical extremes. The system is evaluated on a 10-node Substrate blockchain network storing real smart water meter data hashes from the Queensland Government open data portal.
Statistical analysis across configurations of 4, 7, and 10 active nodes confirms that the controller produces distinct operational profiles reflecting each configuration's provisioning state. In closed-loop experiments, the controller autonomously adjusts validator participation in both directions, activating validators under rising load and removing them under over-provisioning, converging to the same stable equilibrium from both directions.
Compared against three threshold-based baselines, it shows fewer scaling oscillations while maintaining comparable block production times. Results indicate that TS fuzzy inference can support autonomous validator management in private blockchain deployments, with stable scaling behavior that threshold approaches cannot match.
Blogger's Review: This paper successfully implements dynamic management of validator nodes in private blockchains using the Takagi-Sugeno fuzzy inference system, addressing resource wastage and performance bottlenecks associated with fixed node configurations. The system not only proves practical but also offers stable scaling capabilities, showcasing the broad application potential of fuzzy inference in the blockchain domain.