Effective low capacity status prediction for cloud systems
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.
BibTeX
@inproceedings{Dong-al:FSE21,
author = {Hang Dong and
Si Qin and
Yong Xu and
Bo Qiao and
Shandan Zhou and
Xian Yang and
Chuan Luo and
Pu Zhao and
Qingwei Lin and
Hongyu Zhang and
Abulikemu Abuduweili and
Sanjay Ramanujan and
Karthikeyan Subramanian and
Andrew Zhou and
Saravanakumar Rajmohan and
Dongmei Zhang and
Thomas Moscibroda},
title = {Effective low capacity status prediction for cloud systems},
booktitle = {{ESEC/SIGSOFT} {FSE}},
pages = {1236--1241},
publisher = {{ACM}},
year = {2021},
}