  Anomaly detection significantly enhances the robustness of cloud systems.
While neural network-based methods have recently demonstrated strong
advantages, they encounter practical challenges in cloud environments: the
contradiction between the impracticality of maintaining a unique model for each
service and the limited ability to deal with diverse normal patterns by a
unified model, as well as issues with handling heavy traffic in real time and
short-term anomaly detection sensitivity.
  Thus, we propose MACE, a multi-normal-pattern accommodated and efficient
anomaly detection method in the frequency domain for time series anomaly
detection. There are three novel characteristics of it: (i) a pattern
extraction mechanism excelling at handling diverse normal patterns with a
unified model, which enables the model to identify anomalies by examining the
correlation between the data sample and its service normal pattern, instead of
solely focusing on the data sample itself; (ii) a dualistic convolution
mechanism that amplifies short-term anomalies in the time domain and hinders
the reconstruction of anomalies in the frequency domain, which enlarges the
reconstruction error disparity between anomaly and normality and facilitates
anomaly detection; (iii) leveraging the sparsity and parallelism of frequency
domain to enhance model efficiency. We theoretically and experimentally prove
that using a strategically selected subset of Fourier bases can not only reduce
computational overhead but is also profitable to distinguish anomalies,
compared to using the complete spectrum. Moreover, extensive experiments
demonstrate MACE's effectiveness in handling diverse normal patterns with a
unified model and it achieves state-of-the-art performance with high
efficiency.
