Cuzzocrea, Alfredo, Mumolo, Enzo, Hassani, Marwan & Grasso, Giorgio Mario (2018). Towards effective generation of synthetic memory references via markovian models. In Ling Liu, Claudio Demartini, Ji-Jiang Yang, Thomas Conte, Kamrul Hasan, Edmundo Tovar, Zhiyong Zhang, Sheikh Iqbal Ahamed, Stelvio Cimato, Toyokazu Akiyama, Sorel Reisman, William Claycomb, Motonori Nakamura, Hiroki Takakura & Chung-Horng Lung (Eds.), Proceedings – 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018 (pp. 199-203). Piscataway: IEEE Computer Society.
In this paper we introduce a technique for the synthetic generation of memory references which behave as those generated by given running programs. Our approach is based on a novel Machine Learning algorithm we called Hierarchical Hidden/non Hidden Markov Model (HHnHMM). Short chunks of memory references from a running program are classified as Sequential, Periodic, Random, Jump or Other. Such execution classes are used to train an HHnHMM for that program. Trained HHnHMM are used as stochastic generators of memory reference addresses. In this way we can generate in real time memory reference streams of any length, which mimic the behavior of given programs without the need to store anything.