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

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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.

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Acknowledgments

The work is supported by 973 program of National Basic Research Program of China (Grant No. 2011CB302506, 2011CB302704), National Natural Science Foundation of China (Grant No. 61132001, 61001118, 61171102), Program for New Century Excellent Talents in University (Grant No. NCET-11-0592), the National Key Technology Research and Development Program of China “Research on the mobile community cultural service aggregation supporting technology” (2012BAH94F02), and the Novel Mobile Service Control Network Architecture and Key Technologies (Grant No.2010ZX03004-001-01).

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Authors and Affiliations

  1. State Key Lab of Networking and Switching Technology, Beijing University of Posts & Telecommunications, Beijing, 100876, China

    Jingqi Yang, Chuanchang Liu, Yanlei Shang, Bo Cheng, Zexiang Mao, Chunhong Liu, Lisha Niu & Junliang Chen

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  1. Jingqi Yang
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  2. Chuanchang Liu
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  3. Yanlei Shang
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  4. Bo Cheng
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  5. Zexiang Mao
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  6. Chunhong Liu
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  7. Lisha Niu
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  8. Junliang Chen
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Correspondence to Jingqi Yang.

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Yang, J., Liu, C., Shang, Y. et al. A cost-aware auto-scaling approach using the workload prediction in service clouds. Inf Syst Front 16, 7–18 (2014). https://doi.org/10.1007/s10796-013-9459-0

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