A Comprehensive Study of Co-residence Threat in Multi-tenant Public PaaS Clouds

  • Weijuan Zhang
  • Xiaoqi Jia
  • Chang Wang
  • Shengzhi Zhang
  • Qingjia Huang
  • Mingsheng Wang
  • Peng Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9977)


Public Platform-as-a-Service (PaaS) clouds are always multi-tenant. Applications from different tenants may reside on the same physical machine, which introduces the risk of sharing physical resources with a potentially malicious application. This gives the malicious application the chance to extract secret information of other tenants via side-channels. Though large numbers of researchers focus on the information extraction, there are few studies on the co-residence threat in public clouds, especially PaaS clouds. In this paper, we in detail studied the co-residence threat of public PaaS clouds. Firstly, we investigate the characteristics of different PaaS clouds and implement a memory bus based covert-channel detection method that works for various PaaS cloud platforms. Secondly, we study three popular PaaS clouds Amazon Elastic Beanstalk, IBM Bluemix and OpenShift, to identify the co-residence threat in their placement policies. We evaluate several placement variables (e.g., application type, number of the instances, time launched, data center region, etc.) to study their influence on achieving co-residence. The results show that all the three PaaS clouds are vulnerable to the co-residence threat and the application type plays an important role in achieving co-residence on container-based PaaS clouds. At last, we present an efficient launch strategy to achieve co-residence with the victim on public PaaS clouds.


PaaS cloud Co-resident Memory bus Co-residence threat Multi-tenant 



We would like to thank Zeyi Liu and the anonymous reviewers for their insightful and detailed comments. This paper was supported by National Natural Science Foundation of China (NSFC) under Grant No. 61100228 and the project Core Electronic Devices, High-end Generic Chips and Basic Software (No. 2015ZX01029101-001). Peng Liu was supported by NSF CNS-1422594 and NSF SBE-1422215.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Weijuan Zhang
    • 1
    • 2
    • 3
    • 4
  • Xiaoqi Jia
    • 1
    • 2
    • 3
    • 4
  • Chang Wang
    • 2
    • 3
    • 4
  • Shengzhi Zhang
    • 5
  • Qingjia Huang
    • 2
    • 3
    • 4
  • Mingsheng Wang
    • 1
    • 2
  • Peng Liu
    • 6
  1. 1.State Key Laboratory of Information SecurityInstitute of Information Engineering, CASBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Key Laboratory of Network Assessment Technology, IIE, CASBeijingChina
  4. 4.Beijing Key Laboratory of Network Security and Protection TechnologyBeijingChina
  5. 5.School of ComputingFlorida Institute of TechnologyMelbourneUSA
  6. 6.College of Information Sciences and TechnologyThe Pennsylvania State UniversityState CollegeUSA

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