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RISC: Risk Assessment of Instance Selection in Cloud Markets

  • Jingyun Gu
  • Zichen Xu
  • Cuiying Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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%.

Notes

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Nanchang UniversityNanchangChina

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