A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently

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

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    Abstract

    Driven by several real-life case studies and in-lab developments, synthetic memory reference generation has a long tradition in computer science research. The goal is that of reproducing the running of an arbitrary program, whose generated traces can later be used for simulations and experiments. In this paper we investigate this research context and provide principles and algorithms of a Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. Specifically, our approach is based on a novel Machine Learning algorithm we called Hierarchical Hidden/non Hidden Markov Model (HHnHMM). Experimental results conclude this paper..

    Original languageEnglish
    Title of host publicationProceedings - DMSVIVA 2018
    Subtitle of host publicationProceedings of the 24th International DMS Conference on Visualization and Visual Languages
    Place of PublicationPittsburgh
    PublisherKnowledge Systems Institute Graduate School
    Pages83-90
    Number of pages8
    ISBN (Electronic)1891706454, 9781891706455
    DOIs
    Publication statusPublished - 1 Jan 2018
    Event24th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2018 - Redwood City, United States
    Duration: 29 Jun 201830 Jun 2018

    Conference

    Conference24th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2018
    CountryUnited States
    CityRedwood City
    Period29/06/1830/06/18

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