Effective Low Capacity Status Prediction for Cloud Systems - Dong, Hang; Qin, Si; Xu, Yong; Qiao, Bo; Zhou, Shandan; Yang, Xian; Luo, Chuan; Zhao, Pu; 林庆维, Qingwei Lin; Zhang, Hongyu; Abuduweili, Abulikemu; Ramanujan, Sanjay; et al. (ESEC/FSE'21 Industry Track, 2021)
In cloud systems, an accurate capacity planning is very important for cloud provider to improve service availability. Traditional methods simply predicting "when the available resources is exhausted" are not effective due to customer demand fragmentation and platform allocation constraints. In this paper, we propose a novel prediction approach which proactively predicts the level of resource allocation failures from the perspective of low capacity status. By jointly considering the data from different sources in both time series form and static form, the proposed approach can make accurate LCS predictions in a complex and dynamic cloud environment, and thereby improve the service availability of cloud systems. The proposed approach is evaluated by real-world datasets collected from a large scale public cloud platform, and the results confirm its effectiveness.