Nonlinearly correlated failure analysis and autonomic prediction for distributed systems

In order to achieve failure prediction without manual intervention for distributed systems,a novel failure feature analysis and extraction approach to automate failure prediction is proposed.Compared with the traditional methods which focus on building heuristic rules or models,the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns.Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction.In addition,the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction.Based on the runtime monitoring of failure metrics,the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.