Towards effective generation of synthetic memory references via markovian models

Alfredo Cuzzocrea, Enzo Mumolo, Marwan Hassani, Giorgio Mario Grasso

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
EditorsClaudio Demartini, Sorel Reisman, Ling Liu, Edmundo Tovar, Hiroki Takakura, Ji-Jiang Yang, Chung-Horng Lung, Sheikh Iqbal Ahamed, Kamrul Hasan, Thomas Conte, Motonori Nakamura, Zhiyong Zhang, Toyokazu Akiyama, William Claycomb, Stelvio Cimato
Place of PublicationPiscataway
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538626665
Publication statusPublished - 8 Jun 2018
Event42nd IEEE Computer Software and Applications Conference, (COMPSAC 2018) - Tokyo, Japan
Duration: 23 Jul 201827 Jul 2018


Conference42nd IEEE Computer Software and Applications Conference, (COMPSAC 2018)
Abbreviated titleCOMPSAC2018
Internet address


  • Embedded Computing
  • Markovian Models
  • Synthetic Memory References


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