Towards effective generation of synthetic memory references via markovian models

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

doi:10.1109/COMPSAC.2018.10229

Towards effective generation of synthetic memory references via markovian models

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

Process Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
Editors	Claudio 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 Publication	Piscataway
Publisher	IEEE Computer Society
Pages	199-203
Number of pages	5
ISBN (Electronic)	9781538626665
DOIs	https://doi.org/10.1109/COMPSAC.2018.10229
Publication status	Published - 8 Jun 2018
Event	42nd IEEE Computer Software and Applications Conference, (COMPSAC 2018) - Tokyo, Japan Duration: 23 Jul 2018 → 27 Jul 2018 https://www.cloudendure.com/events/compsac-2018-42nd-ieee-computer-society-international-conference-on-computers-software-applications/

Conference

Conference	42nd IEEE Computer Software and Applications Conference, (COMPSAC 2018)
Abbreviated title	COMPSAC2018
Country/Territory	Japan
City	Tokyo
Period	23/07/18 → 27/07/18
Internet address	https://www.cloudendure.com/events/compsac-2018-42nd-ieee-computer-society-international-conference-on-computers-software-applications/

Keywords

Embedded Computing
Markovian Models
Synthetic Memory References

Access to Document

10.1109/COMPSAC.2018.10229

Cite this

Cuzzocrea, A., Mumolo, E., Hassani, M., & Grasso, G. M. (2018). Towards effective generation of synthetic memory references via markovian models. In C. Demartini, S. Reisman, L. Liu, E. Tovar, H. Takakura, J-J. Yang, C-H. Lung, S. I. Ahamed, K. Hasan, T. Conte, M. Nakamura, Z. Zhang, T. Akiyama, W. Claycomb, & S. Cimato (Eds.), Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018 (pp. 199-203). [8377857] IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2018.10229

Cuzzocrea, Alfredo ; Mumolo, Enzo ; Hassani, Marwan et al. / Towards effective generation of synthetic memory references via markovian models. Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018. editor / Claudio 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. Piscataway : IEEE Computer Society, 2018. pp. 199-203

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title = "Towards effective generation of synthetic memory references via markovian models",

abstract = "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.",

keywords = "Embedded Computing, Markovian Models, Synthetic Memory References",

author = "Alfredo Cuzzocrea and Enzo Mumolo and Marwan Hassani and Grasso, {Giorgio Mario}",

year = "2018",

month = jun,

day = "8",

doi = "10.1109/COMPSAC.2018.10229",

language = "English",

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editor = "Claudio Demartini and Sorel Reisman and Ling Liu and Edmundo Tovar and Hiroki Takakura and Ji-Jiang Yang and Chung-Horng Lung and Ahamed, {Sheikh Iqbal} and Kamrul Hasan and Thomas Conte and Motonori Nakamura and Zhiyong Zhang and Toyokazu Akiyama and William Claycomb and Stelvio Cimato",

booktitle = "Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018",

publisher = "IEEE Computer Society",

address = "United States",

note = "42nd IEEE Computer Software and Applications Conference, (COMPSAC 2018), COMPSAC2018 ; Conference date: 23-07-2018 Through 27-07-2018",

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Cuzzocrea, A, Mumolo, E, Hassani, M & Grasso, GM 2018, Towards effective generation of synthetic memory references via markovian models. in C Demartini, S Reisman, L Liu, E Tovar, H Takakura, J-J Yang, C-H Lung, SI Ahamed, K Hasan, T Conte, M Nakamura, Z Zhang, T Akiyama, W Claycomb & S Cimato (eds), Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018., 8377857, IEEE Computer Society, Piscataway, pp. 199-203, 42nd IEEE Computer Software and Applications Conference, (COMPSAC 2018), Tokyo, Japan, 23/07/18. https://doi.org/10.1109/COMPSAC.2018.10229

Towards effective generation of synthetic memory references via markovian models. / Cuzzocrea, Alfredo; Mumolo, Enzo; Hassani, Marwan et al.

Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018. ed. / Claudio 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. Piscataway : IEEE Computer Society, 2018. p. 199-203 8377857.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Towards effective generation of synthetic memory references via markovian models

AU - Cuzzocrea, Alfredo

AU - Mumolo, Enzo

AU - Hassani, Marwan

AU - Grasso, Giorgio Mario

PY - 2018/6/8

Y1 - 2018/6/8

N2 - 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.

AB - 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.

KW - Embedded Computing

KW - Markovian Models

KW - Synthetic Memory References

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M3 - Conference contribution

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EP - 203

BT - Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018

A2 - Demartini, Claudio

A2 - Reisman, Sorel

A2 - Liu, Ling

A2 - Tovar, Edmundo

A2 - Takakura, Hiroki

A2 - Yang, Ji-Jiang

A2 - Lung, Chung-Horng

A2 - Ahamed, Sheikh Iqbal

A2 - Hasan, Kamrul

A2 - Conte, Thomas

A2 - Nakamura, Motonori

A2 - Zhang, Zhiyong

A2 - Akiyama, Toyokazu

A2 - Claycomb, William

A2 - Cimato, Stelvio

PB - IEEE Computer Society

CY - Piscataway

T2 - 42nd IEEE Computer Software and Applications Conference, (COMPSAC 2018)

Y2 - 23 July 2018 through 27 July 2018

ER -

Cuzzocrea A, Mumolo E, Hassani M, Grasso GM. Towards effective generation of synthetic memory references via markovian models. In Demartini C, Reisman S, Liu L, Tovar E, Takakura H, Yang J-J, Lung C-H, Ahamed SI, Hasan K, Conte T, Nakamura M, Zhang Z, Akiyama T, Claycomb W, Cimato S, editors, Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018. Piscataway: IEEE Computer Society. 2018. p. 199-203. 8377857 doi: 10.1109/COMPSAC.2018.10229

Towards effective generation of synthetic memory references via markovian models

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Keywords

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