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Alerts are crucial for requesting prompt human intervention upon cloudanomalies. The quality of alerts significantly affects the cloud reliabilityand the cloud provider's business revenue. In practice, we observe on-callengineers being hindered from quickly locating and fixing faulty cloud servicesbecause of the vast existence of misleading, non-informative, non-actionablealerts. We call the ineffectiveness of alerts "anti-patterns of alerts". Tobetter understand the anti-patterns of alerts and provide actionable measuresto mitigate anti-patterns, in this paper, we conduct the first empirical studyon the practices of mitigating anti-patterns of alerts in an industrial cloudsystem. We study the alert strategies and the alert processing procedure atHuawei Cloud, a leading cloud provider. Our study combines the quantitativeanalysis of millions of alerts in two years and a survey with eighteenexperienced engineers. As a result, we summarized four individual anti-patternsand two collective anti-patterns of alerts. We also summarize four currentreactions to mitigate the anti-patterns of alerts, and the general preventativeguidelines for the configuration of alert strategy. Lastly, we propose toexplore the automatic evaluation of the Quality of Alerts (QoA), including theindicativeness, precision, and handleability of alerts, as a future researchdirection that assists in the automatic detection of alerts' anti-patterns. Thefindings of our study are valuable for optimizing cloud monitoring systems andimproving the reliability of cloud services.

Characterizing and Mitigating Anti-patterns of Alerts in Industrial Cloud Systems