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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jingqi Yang & Chuanchang Liu & Yanlei Shang & Bo Cheng & Zexiang Mao & Chunhong Liu & Lisha Niu & Junliang Chen, 2014. "A cost-aware auto-scaling approach using the workload prediction in service clouds," Information Systems Frontiers, Springer, vol. 16(1), pages 7-18, March.
    • Handle: RePEc:spr:infosf:v:16:y:2014:i:1:d:10.1007_s10796-013-9459-0
    DOI: 10.1007/s10796-013-9459-0
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    Citations

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    Cited by:

    1. Kena Alexander & Muhammad Hanif & Choonhwa Lee & Eunsam Kim & Sumi Helal, 2020. "Cost-aware orchestration of applications over heterogeneous clouds," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.
    2. Shuai Yuan & Sanjukta Das & Ram Ramesh & Chunming Qiao, 2023. "Availability-Aware Virtual Resource Provisioning for Infrastructure Service Agreements in the Cloud," Information Systems Frontiers, Springer, vol. 25(4), pages 1495-1512, August.
    3. Ching-Hsien Hsu & Jianhua Ma & Mohammad S. Obaidat, 2014. "Dynamic intelligence towards merging cloud and communication services," Information Systems Frontiers, Springer, vol. 16(1), pages 1-5, March.

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