Self-healing and Hybrid Diagnosis in Cloud Computing

Authors:
Yuanshun Dai;Yanping Xiang;Gewei Zhang
Affiliations:
Collaborative Autonomic Computing Laboratory, School of Computer Science, University of Electronic Science and Technology of China, and Department of Electrical Engineering and Computer Science, U ...;Collaborative Autonomic Computing Laboratory, School of Computer Science, University of Electronic Science and Technology of China,;Department of Electrical Engineering and Computer Science, University of Tennessee, USA
Venue:
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Year:
2009

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Abstract

Cloud computing requires a robust, scalable, and high-performance infrastructure. To provide a reliable and dependable cloud computing platform, it is necessary to build a self-diagnosis and self-healing system against various failures or downgrades. This paper is the first to study the self-healing function, a challenging topic in today's clouding computing systems, from the consequence-oriented point of view. To fulfill the self-diagnosis and self-healing requirements of efficiency, accuracy, and learning ability, a hybrid tool that takes advantages from Multivariate Decision Diagram and Naïve Bayes Classifier is proposed. An example is used to demonstrate that this proposed approach is effective.