A Biophysically-Inspired Feedback Controller for Multi-Class Cache Fairness

Abstract

Cache replacement under multi-tenant LLM-serving conditions is a multi-class problem: short, high-reuse system prompts; long, moderate-reuse user documents; medium-length code context; and bursty conversation history share a single eviction pool. Under skewed multi-class arrivals, conventional flat-LRU policies expose the worst-served-class miss ratio ($m_{\max}$) only as a fixed point. We introduce a class of cache-replacement policies parameterised by a per-class flux formula, where three structural commitments -- a single global token-mass imbalance signal, $K$ parallel rectified per-class promotion accumulators, and an age-ordered eviction backstop -- produce emergent multi-class fairness. We instantiate this class with a linear V-coupled rectified flux and a Goldman-Hodgkin-Katz extension whose $V \to 0$ limit is exactly the linear form. Across four skew levels on synthetic multi-class workloads, the policy class closes 27--72\,\% of the LRU$\to$Belady gap on $m_{\max}$, with linear and GHK interchangeable on the headline objective within search variance. The fairness/throughput tradeoff is exposed as a tunable knob on a single hyperparameter axis. We position this against the LeCaR feedback-controller lineage and the formal-control-theory cache-decay lineage as a novel combination of known ingredients. Code and reproduction scripts: https://github.com/flatmax/membrane.cache