With the software applications increasing in complexity, description of hardware is becoming increasingly relevant. To ensure the quality of service for specific applications, it is imperative to have an insight into hardware resources. Cache memory is used for storing data closer to the processor needed for quick access and improves the quality of service of applications. The description of cache memory usually consists of the size of different cache levels, set associativity, or line size. Software applications would benefit more from a more detailed model of cache memory.In this thesis, we offer a way of describing the behavior of cache memory which benefits software performance. Several performance events are tested, including L1 cache misses, L2 cache misses, and L3 cache misses. With the collected information, we develop performance models of cache memory behavior. Goodness of fit is tested for these models and they are used to predict the behavior of the cache memory during future runs of the same application.Our experiments show that L1 cache misses can be modeled to predict the future runs. L2 cache misses model is less accurate but still usable for predictions, and L3 cache misses model is the least accurate and is not feasible to predict the behavior of the future runs.