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International Conference on Algorithms and Architectures for Parallel Processing

ICA3PP 2018: Algorithms and Architectures for Parallel Processing pp 59–74Cite as

RISC: Risk Assessment of Instance Selection in Cloud Markets

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11334))

Abstract

Cloud markets provide instances as products in Infrastructure-as-a-Service (IaaS). Users usually underprovision instances while risking the possible failure of SLOs, or overprovision resources by suffering higher expenses. The underlying key nature of user behavior in purchasing instances can be essential for maximizing cloud market profits. However, for cloud service providers, there is little knowledge on assessing the risk of user choices on cloud instances. This paper proposes one of the first studies on the risk assessment in IaaS cloud markets. We first provide a modeling process to understand user and violations of SLOs, from server statistics. To understand the risk, we propose RISC, a mechanism to assess the risk of instance selection. RISC contains an analytic hierarchy process to evaluate the decisions, an optimization process to expose the risk frontier, and a feedback approach to fine-tuning the instance recommendation. We have evaluated our approach using simulations on real-world workloads and cloud market statistics. The results show that, compared to traditional approaches, our approach provides the best tradeoff between SLOs and costs, as it can maximize the overall profit up to 5X for the cloud service provider. All users achieve their SLOs goals while minimizing their average expenses by 34.6%.

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Acknowledgement

This research was supported by the grant from the Tencent Rhino Grant award (11002675), by the grant from the National Science Foundation China (NSFC) (617022501006873), and by the grant from Jiangxi Province Science Foundation for Youths (708237400050).

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Authors and Affiliations

  1. Nanchang University, Nanchang, China

    Jingyun Gu, Zichen Xu & Cuiying Gao

Authors
  1. Jingyun Gu
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  2. Zichen Xu
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  3. Cuiying Gao
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Corresponding authors

Correspondence to Jingyun Gu , Zichen Xu or Cuiying Gao .

Editor information

Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Guangzhou University, Guangzhou, China

    Jin Li

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Gu, J., Xu, Z., Gao, C. (2018). RISC: Risk Assessment of Instance Selection in Cloud Markets. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11334. Springer, Cham. https://doi.org/10.1007/978-3-030-05051-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-05051-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05050-4

  • Online ISBN: 978-3-030-05051-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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