Paper Info
Title
Effective low capacity status prediction for cloud systems
Abstract
ABSTRACTIn 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.
Year
DOI
Venue
2021
10.1145/3468264.3473917
Foundations of Software Engineering
Keywords
DocType
Citations 
software reliability, cloud computing, capacity prediction, feature embedding
Conference
0
PageRank 
References 
Authors
0.34
0
17
Name
Order
Citations
PageRank
Hang Dong100.68
Si Qin234.16
Yong Xu3413.21
Bo Qiao4339.09
Shandan Zhou500.34
Xian Yang693.92
Chuan Luo749641.38
Pu Zhao887.23
Qingwei Lin928527.76
Hongyu Zhang1086450.03
Abulikemu Abuduweili1100.34
Sanjay Ramanujan1200.34
Karthikeyan Subramanian1300.34
Andrew Zhou1401.35
Saravanakumar Rajmohan1513.39
Dongmei Zhang161439132.94
Thomas Moscibroda174047200.40