Towards Effective Generation of Synthetic Memory References Via Markovian Models
| Autor: | Cuzzocrea, Alfredo, Mumolo, Enzo, Hassani, Marwan, Grasso, Giorgio Mario, Demartini, Claudio, Reisman, Sorel, Liu, Ling, Tovar, Edmundo, Takakura, Hiroki, Yang, Ji-Jiang, Lung, Chung-Horng, Ahamed, Sheikh Iqbal, Hasan, Kamrul, Conte, Thomas, Nakamura, Motonori, Zhang, Zhiyong, Akiyama, Toyokazu, Claycomb, William, Cimato, Stelvio |
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| Přispěvatelé: | IEEE, Cuzzocrea, Alfredo, Mumolo, Enzo, Hassani, Marwan, Grasso, Giorgio Mario, Process Science |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Theoretical computer science
Computer science memory references ergodic HMM Markov process 02 engineering and technology memory reference Embedded Computing Markovian Models Synthetic Memory References symbols.namesake Software 0202 electrical engineering electronic engineering information engineering Spectral analysis Hidden Markov model 020203 distributed computing execution classe business.industry execution classes spectral analysis Trace-driven simulation synthetic memory references symbols 020201 artificial intelligence & image processing business synthetic memory reference |
| Zdroj: | COMPSAC (2) Proceedings-2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018, 199-203 STARTPAGE=199;ENDPAGE=203;TITLE=Proceedings-2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018 |
| Popis: | Trace-driven simulation is a popular technique useful in many applications, as for example analysis of memory hierarchies or internet subsystems, and to evaluate the performance of computer systems. Normally, traces should be gathered from really working systems. However, real traces require enormous memory space and time. An alternative is to generate Synthetic traces using suitable algorithms. In this paper we describe an algorithm 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, wich mimic the behaviour of given programs without the need to store anything. It is worth noting that our approach can be extended to other applications, for example network or data storage systems. In this paper we address only the generation of synthetic memory references generated by instruction fetches. Experimental results and a case study conclude this paper. |
| Databáze: | OpenAIRE |
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