Recently, chip multiprocessors (CMPs) that can simultaneously execute multiple workloads using multiple cores have become a key to achieve high-performance processing. To improve CMP performance, various shared resource management mechanisms have been proposed. In particular, cache partitioning is significantly effective to avoid resource conflicts at a shared cache memory. As most cache partitioning methods need to predict the changes in cache access characteristics of each workload when the cache partition moves, it is important for cache partitioning to establish an accurate prediction model. In this paper, we first analyze the cache access locality of various applications using stack distance profiling. We figure out that stack distance distributions incline to obey socalled Zipf's law. To achieve effective cache partitioning, then, we propose a model based on Zipf's law that predicts the changes in the stack distance distributions. Using the model, we also show the validity of a measure, which has been proposed in our previous work to quantify how much a workload demands the cache capacity.