on reference string generation processes
Efficient memory management is important for optimizing computer usage. Intuition, simulation, experience, and analysis have contributed to the design of space management algorithms. Analytical models require accurate and concise descriptions of the system's environment. The referencing pattern, describing the sequence of memory addresses, is the environment for memory management problems. One referencing model assigns probabilities to positions in an LRU stack of memory pages. Local behavior is easily described using this model. However, the distinctly different behavior among instruction and data references is lost. In this paper we generalize the LRU stack model to an arbitrary number of memory spaces which are selected by the transitions of a Markov chain. The additional detail affects the behavior of the models for a working set management strategy.
reference strings strings generation generation process memory management memorialized management optimality computation simulation experience analysis design space algorithm model systems environments pattern sequence probability lru behavior instruction data paper transit affect working set strategies markov models replacement policies general stack distance