一句话：提出从低容量状态角度主动预测云系统资源分配失败水平的方法，以改善容量规划与服务可用性。
云系统中准确的容量规划对提升服务可用性至关重要。传统方法仅预测“可用资源何时耗尽”，但受客户需求碎片化和平台分配约束影响，效果不佳。本文提出一种新的预测方法，从低容量状态（LCS）角度主动预测资源分配失败的水平。该方法联合利用来自不同数据源的时间序列数据和静态数据，从而在复杂动态的云环境中进行准确的 LCS 预测，并据此改善云系统服务可用性。作者使用从大规模公有云平台收集的真实数据集进行评估，结果验证了所提方法的有效性。
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.
DOI 原文 ·
@inproceedings{paperbot6453,
title = {Effective low capacity status prediction for cloud systems},
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},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3473917}
}