@inproceedings{dong2021effective,
author = {Dong, Hang and Qin, Si and Xu, Yong and Qiao, Bo and Zhou, Shandan and Yang, Xian and Luo, Chuan and Zhao, Pu and 林庆维, Qingwei Lin and Zhang, Hongyu and Abuduweili, Abulikemu and Ramanujan, Sanjay and Subramanian, Karthikeyan and Zhou, Andrew and Rajmohan, Saravanakumar and Zhang, Dongmei and Moscibroda, Thomas},
title = {Effective Low Capacity Status Prediction for Cloud Systems},
booktitle = {ESEC/FSE'21},
year = {2021},
month = {August},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/effective-low-capacity-status-prediction-for-cloud-systems/},
pages = {1236-1241},
}