Memory Pressure Amplification in Declarative Runtimes

Abstract

Declarative application runtimes, widely used in rule engines, workflow systems, and low-code platforms, introduce abstraction-driven execution models that obscure underlying memory behavior and lead to disproportionate resource consumption. Unlike imperative systems, these runtimes generate intermediate states, repeated evaluations, and transient objects that collectively amplify memory usage beyond input workload characteristics. This study presents a formal analysis of memory pressure amplification by modeling allocation patterns, state expansion, and object lifecycle dynamics in declarative execution environments. Key metrics such as memory amplification factor, allocation churn rate, and garbage collection overhead are defined to quantify amplification effects. Experimental evaluation across varying workload complexities demonstrates non-linear memory growth, increased allocation bursts, and elevated garbage collection frequency, resulting in reduced runtime stability. Comparative analysis with optimized memory management strategies shows that lifecycle-aware allocation and state pruning significantly mitigate amplification and improve system efficiency. The findings highlight the need for integrating memory-aware execution strategies into declarative runtimes to ensure scalable and predictable performance in enterprise systems.