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We develop a reuse distance/stack distance based analytical modeling framework for efficient, online prediction of cache performance for a range of cache configurations and replacement policies LRU, PLRU, RANDOM, NMRU. Our framework unifies existing cache miss rate prediction techniques such as Smith's associativity model, Poisson variants, and hardware way-counter based schemes. We also show how to adapt LRU way-counters to work when the number of sets in the cache changes. As an example application, we demonstrate how results from our models can be used to select, based on workload access characteristics, last-level cache configurations that aim to minimize energy-delay product.