As one of the most critical cloud products, the stability of cloud servers is of paramount importance. Traditionally, stability is evaluated based on downtime, i.e., the duration when cloud servers are unavailable. However, through our extensive engagement in stability-related work, we have discovered that only 27% of stability issues are related to unavailability, which is in fact a subset of stability. Therefore, in this paper, we propose the Comprehensive Damage Indicator (CDI), comprising three distinct sub-metrics: the Unavailability Indicator, Performance Indicator, and Control-plane Indicator. The CDI is able to evaluate the stability of large-scale cloud servers more comprehensively. In the production environment of Alibaba Cloud, the CDI has been implemented on top of Apache Spark. Over the past two years, we have employed the CDI to guide our stability-related works, including architecture comparison, potential problem detection and operation action optimization. The overall results are very favorable. Taking Fiscal Year 2024 as an example, the three sub-metrics have respectively decreased by 40%, 80%, and 35%.

Seasonal-trend decomposition (STD) is a crucial task in time series data analysis. Due to the challenges of scalability, there is a pressing need for an ultra-fast online algorithm. However, existing algorithms either fail to handle long-period time series (such as OnlineSTL), or need time-consuming iterative processes (such as OneShotSTL). Therefore, we propose BacktrackSTL, the first non-iterative online STD algorithm with period-independent O(1) update complexity. It is also robust to outlier, seasonality shift and trend jump because of the combination of outlier-resilient smoothing, non-local seasonal filtering and backtrack technique. Experimentally, BacktrackSTL decomposes a value within 1.6 μs, which is 15X faster than the state-of-the-art online algorithm OneShotSTL, while maintaining comparable accuracy to the best offline algorithm RobustSTL. We have also deployed BacktrackSTL on the top of Apache Flink to decompose monitoring metrics in Alibaba Cloud for over a year. Besides, we have open-sourced the artifact of this proposal on GitHub.