A cost-aware auto-scaling approach using the workload prediction in service clouds

Jingqi Yang (), Chuanchang Liu, Yanlei Shang, Bo Cheng, Zexiang Mao, Chunhong Liu, Lisha Niu and Junliang Chen
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Jingqi Yang: Beijing University of Posts & Telecommunications
Chuanchang Liu: Beijing University of Posts & Telecommunications
Yanlei Shang: Beijing University of Posts & Telecommunications
Bo Cheng: Beijing University of Posts & Telecommunications
Zexiang Mao: Beijing University of Posts & Telecommunications
Chunhong Liu: Beijing University of Posts & Telecommunications
Lisha Niu: Beijing University of Posts & Telecommunications
Junliang Chen: Beijing University of Posts & Telecommunications

Information Systems Frontiers, 2014, vol. 16, issue 1, No 2, 7-18

Abstract: Abstract Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.

Keywords: Service cloud; Scalability; Workload prediction; Cost-aware (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10796-013-9459-0

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