• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Identifying SDC-Causing Instructions Based on Random Forests Algorithm

Vol. 13, No.3, March 31, 2019
10.3837/tiis.2019.03.025, Download Paper (Free):

Abstract

Silent Data Corruptions (SDCs) is a serious reliability issue in many domains of computer system. The identification and protection of the program instructions that cause SDCs is one of the research hotspots in computer reliability field at present. A lot of solutions have already been proposed to solve this problem. However, many of them are hard to be applied widely due to time-consuming and expensive costs. This paper proposes an intelligent approach named SDCPredictor to identify the instructions that cause SDCs. SDCPredictor identifies SDC-causing Instructions depending on analyzing the static and dynamic features of instructions rather than fault injections. The experimental results demonstrate that SDCPredictor is highly accurate in predicting the SDCs proneness. It can achieve higher fault coverage than previous similar techniques in a moderate time cost.


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Cite this article

[IEEE Style]
LiPing Liu, LinLin Ci, Wei Liu and Hui Yang, "Identifying SDC-Causing Instructions Based on Random Forests Algorithm," KSII Transactions on Internet and Information Systems, vol. 13, no. 3, pp. 1566-1582, 2019. DOI: 10.3837/tiis.2019.03.025

[ACM Style]
Liu, L., Ci, L., Liu, W., and Yang, H. 2019. Identifying SDC-Causing Instructions Based on Random Forests Algorithm. KSII Transactions on Internet and Information Systems, 13, 3, (2019), 1566-1582. DOI: 10.3837/tiis.2019.03.025