An Optimized Iterative Partitioning Model for Predicting Computer System Failures Based on Deep Learning

Computer systems are becoming increasingly complex, they are growing dynamically due to the mobility of their devices, multi-operating environments, frequent updates and upgrades. Conventional reliability theory and conventional methods rarely take into account the actual state of the system; thus, they are not able to reflect the dynamics of the execution systems and the failure processes. The variety of models used illustrate the lack of consensus on how to apprehend this phenomenon, especially the difficulties to formulate a general model of interpretation. Moreover, this diversity of approaches has been developed mainly to diagnosis computer systems. Consequently, the nature of the information available on these systems and the type of defects to be detected lead to the implementation of specific strategies. The main objective of this paper is to use an Iterative Partitioning Log Mining algorithm (IPLOM) to cluster logs events before they are processed further by an optimized deep learning model to detect anomalies form large amount of IBM BlueGene/L collected data. ALL the features are classified by the score and also selected to be saved or even removed from the dataset. The procedures are frequently univariate and consider the feature independently, or with regard to the dependent variable. In such methods, the subset selection process is independent of the learning algorithm, and it is a pre-processing step. Generally, this work describes how to optimize deep learning models in different types of machine learning problems. We started by developing a mining model, and then apply a filter to the input data in order to eliminate the correlated information. Then, we compared the results of these different models using an elevation curves graph. Finally, we extracted additional knowledge from the underlying mining structure. This leads to optimize learning features and to reduce the classification time and even improve the performance of systems’ rules, particularly in resolving complex problems involving a large number of input data.

[1]  Miroslaw Malek,et al.  A survey of online failure prediction methods , 2010, CSUR.

[2]  Stella Morris,et al.  Effect of smart meter data collection frequency in an early detection of shorter-duration voltage anomalies in smart grids , 2019 .

[3]  Poonam Ghuli.  Anomaly Detection in Log Records , 2018 .

[4]  Noura Aknin,et al.  Multidimensional Approach Based on Deep Learning to Improve the Prediction Performance of DNN Models , 2019, Int. J. Emerg. Technol. Learn..

[5]  Cristina Nita-Rotaru,et al.  A survey of attack and defense techniques for reputation systems , 2009, CSUR.

[6]  Mohamed Abid,et al.  Outlier detection approaches for wireless sensor networks: A survey , 2017, Comput. Networks.

[7]  Bill P. Buckles,et al.  Dynamic scene modelling and anomaly detection based on trajectory analysis , 2014 .

[8]  Luiz Eduardo Soares de Oliveira,et al.  Toward a reliable anomaly-based intrusion detection in real-world environments , 2017, Comput. Networks.

[9]  José M. Molina López,et al.  Anomaly Detection Based on Sensor Data in Petroleum Industry Applications , 2015, Sensors.

[10]  Ehud D. Karnin,et al.  A simple procedure for pruning back-propagation trained neural networks , 1990, IEEE Trans. Neural Networks.

[11]  Michael P. Craven,et al.  The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer pe , 1998 .

[12]  Salvatore J. Stolfo,et al.  Using artificial anomalies to detect unknown and known network intrusions , 2003, Knowledge and Information Systems.

[13]  Carl E. Landwehr,et al.  Basic concepts and taxonomy of dependable and secure computing , 2004, IEEE Transactions on Dependable and Secure Computing.

[14]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[15]  David J. Hill,et al.  Anomaly detection in streaming environmental sensor data: A data-driven modeling approach , 2010, Environ. Model. Softw..