用模糊推理动态调节私有链验证节点数，避免资源浪费或性能下降。
Closed-Loop Dynamic Validator Node Scaling in Private Substrate Blockchains Using Takagi-Sugeno Fuzzy Inference
- 基于泰卡吉-杉田模糊系统，根据区块生成时间等参数实时判断是否增减节点。
- 在10节点Substrate链上测试，能稳定收敛至均衡状态，减少频繁震荡。
- 适合需要自动运维的私有区块链场景，尤其对负载波动大的应用有用。
私有区块链网络通常采用固定节点配置，无法适应负载变化。节点过多会浪费资源，过少则导致区块生成延迟、终局性下降。合适的验证节点数量难以确定，因影响因素复杂且随时间变化。本文提出一种泰卡吉-杉田（TS）模糊推理系统，实时读取区块生成时间、区块大小和活跃节点数等链上参数，输出连续效率评分及扩容、维持或缩容建议。控制器采用三角形隶属函数，通过27条规则与积型归约算子进行推理。关键贡献在于基于实际测试环境重新校准隶属函数，使语言变量锚定于观测范围而非理论极值。在10节点Substrate网络上评估，存储昆士兰政府开放数据门户的真实智能水表哈希数据。统计分析显示，4、7、10节点配置下控制器能区分不同资源配置状态。闭环实验中，控制器可双向自动调节验证者参与度，在负载上升时激活节点，过载时移除节点，从任一方向均收敛至相同稳定平衡点。相比三种阈值基线，其振荡更少，同时保持相近的区块生成时间。结果表明，TS模糊推理可支持私有链中自主的验证节点管理，实现稳定扩展行为，优于传统阈值方法。
原文摘要 · Abstract (English)
Private blockchain networks run with fixed node configurations that cannot adapt to changing workload conditions. Too many nodes serving a light workload waste resources; too few nodes facing heavy demand slow block production and degrade finalisation. The right validator count is hard to determine, 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 uses 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 to 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 show that TS fuzzy inference can support autonomous validator management in private blockchain deployments, with stable scaling behaviour threshold approaches cannot match.
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