A Modified Locality Matrix Model (MLMM) - dynamic clustering in a demand paging environment

Abstract

An algorithm is presented which dynamically clusters pages of a problem program based on its post program behavior (i.e. reference string patterns) in a demand paged virtual memory environment. The objective of this algorithm is to minimize the number of page faults during execution, while at the same time use memory page frames efficiently. Dynamic clusters of “time and reference” related pages are built during a program's execution time. The Modified Locality Matrix Model is used to determine inherent program locality and to predict independent dynamic program behavior, separating instruction from data references. Furthermore, strength coefficients between weakly or loosely coupled clusters are used to refine the cluster population, identify cluster transitions, as well as indicate the behavior of the cluster formations.