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

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• 作者：Jingqi Yang (1)
  Chuanchang Liu (1)
  Yanlei Shang (1)
  Bo Cheng (1)
  Zexiang Mao (1)
  Chunhong Liu (1)
  Lisha Niu (1)
  Junliang Chen (1)
  
• 关键词：Service cloud ; Scalability ; Workload prediction ; Cost ; aware
• 刊名：Information Systems Frontiers
• 出版年：2014
• 出版时间：March 2014
• 年：2014
• 卷：16
• 期：1
• 页码：7-18
• 全文大小：1,689 KB
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  14. Saripalli, P., Kiran, G., Shankar, R., Narware, H., Bindal, N. (2011). Load prediction and hot spot detection models for autonomic cloud computing. / In Proceedings of the 2011 4th IEEE international conference on utility and cloud computing (pp. 397鈥?02).
  15. Wang, W., Chen, H., Chen, X. (2012). An availability-aware approach to resource placement of dynamic scaling in clouds. / In Proceedings of the 2012 IEEE 5th international conference on cloud computing (pp. 930鈥?31).
  
• 作者单位：Jingqi Yang (1)
  Chuanchang Liu (1)
  Yanlei Shang (1)
  Bo Cheng (1)
  Zexiang Mao (1)
  Chunhong Liu (1)
  Lisha Niu (1)
  Junliang Chen (1)
  
  1. State Key Lab of Networking and Switching Technology, Beijing University of Posts & Telecommunications, Beijing, 100876, China
  
• ISSN：1572-9419

文摘

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.