Machine Learning
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Pinpoint: Problem Determination in Large, Dynamic Internet Services
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Using Multidimensional Databases for Problem Determination and Planning of a Networked Application
SMW '98 Proceedings of the IEEE Third International Workshop on Systems Management
An introduction to variable and feature selection
The Journal of Machine Learning Research
Failure Diagnosis Using Decision Trees
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
A comparative study of pairwise regression techniques for problem determination
CASCON '07 Proceedings of the 2007 conference of the center for advanced studies on Collaborative research
Fast extraction of adaptive change point based patterns for problem resolution in enterprise systems
DSOM'06 Proceedings of the 17th IFIP/IEEE international conference on Distributed Systems: operations and management
ERMIS: designing, developing, and delivering a remote managed infrastructure services solution
IBM Journal of Research and Development
| Hi-index | 0.00 |
Problem determination and resolution PDR is the process of detecting anomalies in a monitored system, locating the problems responsible for the issue, determining the root cause and fixing the cause of the problem. The cost of PDR represents a substantial part of operational costs, and faster, more effective PDR can contribute to a substantial reduction in system administration costs. In this paper, we propose to automate the process of PDR by leveraging machine learning methods. The main focus is to effectively categorize the problem a user experiences by recognizing the problem specificity leveraging all available training data such like the performance data and the logs data. Specifically, we transform the structure of the problem into a hierarchy which can be determined by existing taxonomy in advance. We then propose an efficient hierarchical incremental learning algorithm which is capable of adjusting its internal local classifier parameters in real-time. Comparing to the traditional batch learning algorithms, this online learning framework can significantly decrease the computational complexity of the training process by learning from new instances on an incremental fashion. In the same time this reduces the amount of memory required to store the training instances. We demonstrate the efficiency of our approach by learning hierarchical problem patterns for several issues occurring in distributed web applications. Experimental results show that our approach substantially outperforms previous methods.