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Monitoring is the key to guarantee the reliability of cloud computing systems. By analyzing monitoring data, administrators can understand systems’ statuses to detect, diagnose and solve problems. However, due to the enormous scale and complex structure of cloud computing, a monitoring system should collect, transfer, store and process a large amount of monitoring data, which brings a significant performance overhead and increases the difficulty of analyzing useful information. To address the above issue, this paper proposes a self-adaptive monitoring approach for cloud computing systems. First, we conduct correlation analysis between different metrics, and monitor selected important ones which represent the others and reflect the running status of a system. Second, we characterize the running status with Principal Component Analysis (PCA), estimate the anomaly degree, and predict the possibility of faults …