Contextual Self-attentive Temporal Point Process for Physical Decommissioning Prediction of Cloud Assets - Yang, Fan; Zhang, Jue; Wang, Lu; Qiao, Bo; Weng, Di; Qin, Xiaoting; Weber, Gregory; Das, D. N.; Rakhunathan, Srinivasan; Srikanth, Ranganathan; 林庆维, Qingwei Lin; Zhang, Dongmei (KDD'23 ADS, 2023)
As cloud computing continues to expand globally, the need for effective management of decommissioned cloud assets in data centers becomes increasingly important. This work focuses on predicting the physical decommissioning date of cloud assets as a crucial component in reverse cloud supply chain management and data center warehouse operation. The decommissioning process is modeled as a contextual self-attentive temporal point process, which incorporates contextual information to model sequences with parallel events and provides more accurate predictions with more seen historical data. We conducted extensive offline and online experiments in 20 sampled data centers. The results show that the proposed methodology achieves the best performance compared with baselines and improves remarkable 94% prediction accuracy in online experiments. This modeling methodology can be extended to other domains with similar workflow-like processes.