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Fast and accurate failure detection is becoming essential in managing large scale Internet services. This paper proposes a novel detection approach based on the subspace mapping between system inputs and internal measurements. By exploring these contextual dependencies, our detector can initiate repair actions accurately, increasing the availability of system. While a classical statistical method, the <em>canonical correlation analysis</em> (CCA), is presented in the paper to achieve subspace mapping, we also propose a more advanced technique, the <em>principal canonical correlation analysis</em> (PCCA), to improve the performance of CCA based detector. PCCA extracts a principal subspace from internal measurements that is not only highly correlated with the inputs, but also a significant representative of original measurements. Experimental results on a J2EE based web application demonstrate that such property of PCCA is especially beneficial to failure detection tasks.
failure detection, subspace mapping, correlation analysis, autonomic computing, Internet services
Guofei Jiang, Kenji Yoshihira, Haifeng Chen, "Failure Detection in Large-Scale Internet Services by Principal Subspace Mapping", IEEE Transactions on Knowledge & Data Engineering, vol. 19, no. , pp. 1308-1320, October 2007, doi:10.1109/TKDE.2007.190633
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