Cloud Resource Autoscaling System based on Hidden Markov Model (HMM)

被引：19
|
作者：
Nikravesh, Ali Yadavar [1 ]
Ajila, Samuel A. [1 ]
Lung, Chung-Horng [1 ]
机构：
[1] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC) | 2014年
关键词：
Cloud computing; Resource provisioning; Proactive auto-scaling; Hidden markov model;
D O I：
10.1109/ICSC.2014.43
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The elasticity characteristic of cloud computing enables clients to acquire and release resources on demand. This characteristic reduces clients' cost by making them pay for the resources they actually have used. On the other hand, clients are obligated to maintain Service Level Agreement (SLA) with their users. One approach to deal with this cost-performance trade-off is employing an auto-scaling system which automatically adjusts application's resources based on its load. In this paper we have proposed an auto-scaling system based on Hidden Markov Model (HMM). We have conducted an experiment on Amazon EC2 infrastructure to evaluate our model. Our results show HMM can generate correct scaling actions in 97% of time. CPU utilization, throughput, and response time are being considered as performance metrics in our experiment.
引用
收藏
页码：124 / 127
页数：4
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