Diffusion-based Time Series Data Imputation for Microsoft 365
Fangkai Yang Wenjie Yin Lu Wang Tianci Li Pu Zhao Bo Liu Paul Wang Bo Qiao Yudong Liu Mårten Björkman Saravan Rajmohan Qingwei Lin Dongmei Zhang
Reliability is extremely important for large-scale cloud systems like
Microsoft 365. Cloud failures such as disk failure, node failure, etc. threaten
service reliability, resulting in online service interruptions and economic
loss. Existing works focus on predicting cloud failures and proactively taking
action before failures happen. However, they suffer from poor data quality like
data missing in model training and prediction, which limits the performance. In
this paper, we focus on enhancing data quality through data imputation by the
proposed Diffusion+, a sample-efficient diffusion model, to impute the missing
data efficiently based on the observed data. Our experiments and application
practice show that our model contributes to improving the performance of the
downstream failure prediction task.