Skip to main content

Advertisement

SpringerLink
Log in

DFA-VMP: An efficient and secure virtual machine placement strategy under cloud environment

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

The problem of Virtual Machine (VM) placement is critical to the security and efficiency of the cloud infrastructure. Nowadays most research focuses on the influences caused by the deployed VM on the data center load, energy consumption, resource loss, etc. Few works consider the security and privacy issues of the tenant data on the VM. For instance, as the application of virtualization technology, the VM from different tenants may be placed on one physical host. Hence, attackers may steal secrets from other tenants by using the side-channel attack based on the shared physical resources, which will threat the data security of the tenants in the cloud computing. To address the above issues, this paper proposes an efficient and secure VM placement strategy. Firstly, we define the related security and efficiency indices in the cloud computing system. Then, we establish a multi-objective constraint optimization model for the VM placement considering the security and performance of the system, and find resolution towards this model based on the discrete firefly algorithm. The experimental results in OpenStack cloud platform indicates that the above strategy can effectively reduce the possibility of malicious tenants and targeted tenants on the same physical node, and reduce energy consumption and resource loss at the data center.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Armbrust M, Fox A, Griffith R et al (2010) A view of cloud computing. Commun ACM 53(4):50–58

    Article  Google Scholar 

  2. Kang C, Wei-Ming Z (2009) Cloud computing: system instances and current research. J Softw 20(5):1337–1348 (in Chinese)

    Google Scholar 

  3. The NIST Definition of Cloud Computing, http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf, 2015, 9, 4

  4. Ristenpart T (2009) Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds. [C] CCS

  5. Vattikonda B, Das S, Shacham H (2011) Eliminating fine grained timers in Xen. In: Proceedings of 3rd ACM workshop on cloud computing security workshop (CCSW 2011), pp 41–46

  6. Wu J, Ding L, Lin Y, Min Allah N, Wang Y (2012) XenPump: a new method to mitigate timing channel in cloud computing. In: Proceedings of 5th IEEE international conference on cloud computing (CLOUD 2012), pp 678–685

  7. Aviram A, Hu S, Ford B, Gummadi R (2010) Determinating timing channels in compute clouds. In: Proceedings of ACM workshop on cloud computing security workshop, pp 103–108

  8. Shi J, Song X, Chen H, Zang B. (2011) Limiting cache-based side-channel in multi-tenant cloud using dynamic page coloring. In: Proceedings of 41st annual IEEE/IFIP international conference on dependable systems and networks workshops (DSN-W 2011), pp 194–199

  9. Keller K, Szefer J, Rexford J, Lee RB (2010) NoHype: virtualized cloud infrastructure without the virtualization. In: Proceedings of 37th international symposium on computer architecture (ISCA’ 10). Saint-Malo, pp 350–361

  10. Szefer J, Keller E, Lee RB, Rexford J (2011) Eliminating the hypervisor attack surface for a more secure cloud. In: Proceedings of 18th ACM conference on computer and communications security (CCS’ 11). Chicago, pp 401–412

  11. Coffman J, Garey MR, Johnson DS (1997) Approximation algorithms for bin packing: a survey. Approximation algorithms for Np-Hard problems. PWS Publishing, Boston, pp 46–93

    Google Scholar 

  12. Shieh A, Kandula S, Greenberg A, Kim C (2010) Seawall, performance isolation for cloud datacenter networks. In: Proceedings 2nd USENIX conference on hot topics in cloud computing (HotCloud’ 10). Boston, pp 1–1

  13. Raj H, Nathuji R, Singh A, England P (2009) Resource management for isolation enhanced cloud services. In: Proceedings of ACM workshop on cloud computing security (CCSW’ 09). Chicago, pp 77–84

  14. Gupta D, Cherkasova L, Gardner R, Vahdat A (2006) Enforcing performance isolation across virtual machines in Xen. In: Proceedings of ACM/IFIP/USENIX international conference on middleware (Middleware’ 06). Melbourne, pp 342–362

  15. Garfinkel T, Pfaff B, Chow J, Rosenblum M, Boneh D (2003) Terra: a virtual machine-based platform for trusted computing. In: Proceedings of 19th ACM symposium on operating systems principles (SOSP’ 03). Bolton Landing, pp 193–206

  16. Sailer R, Jaeger T, Valdez E, Caceres R, Perez R, Berger S, Griffin J L (2005) Building a MAC-based security architecture for the Xen open-source hypervisor. In: Proceedings of the 21st annual computer security applications conferences. Tucson, pp 276– 285

  17. Han Y, Chan J, Alpcan T, Leckie C (2014) Virtual machine allocation policies against co-resident attacks in cloud computing. In: Proceedings IEEE international conference on communications (ICC 2014), pp 786–792

  18. Han Y, Chan J, Alpcan T, Leckie C (2015) Using virtual machine allocation policies to defend against co-resident attacks in cloud computing. Proc IEEE Trans Dependable Secure Comput

  19. Dian S (2012) Research and implementation of virtual machine security placement mechanism in cloud computing. [D]. South East University, Nan Jing

    Google Scholar 

  20. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Futur Gener Comput Syst 28(5):755– 768

    Article  Google Scholar 

  21. Mao-Lin T, Shen-chen P (2015) A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural Process Lett 41(2):211–221

    Article  Google Scholar 

  22. Jamali S, Malektaji S (2014) Improving grouping genetic algorithm for virtual machine placement in cloud data centers. In: Proceedings of 4th international conference on computer and knowledge engineering (ICCKE). Mashhad, pp 328– 333

  23. Liu C, Chen-Yang S, Si-Tian L et al (2014) A new evolutionary multi-objective algorithm to virtual machine placement in virtualized data center. In: Proceedings of 5th IEEE international conference on software engineering and service science (ICSESS). Beijing, pp 272–275

  24. Hendtlass T, Moser I, Randall M. (2009) Dynamic problems and nature inspired meta-heuristics. Springer, Heidelberg

    Book  Google Scholar 

  25. Yong-Qiang G, Hai-Bing G, Zheng-Wei Q et al (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79(8):1230– 1242

    Article  MathSciNet  Google Scholar 

  26. Xu B, Zhi-Ping P, Fang-Xiong X et al (2015) Dynamic deployment of virtual machines in cloud computing using multi-objective optimization. Soft Comput 19(8):2265–2273

    Article  Google Scholar 

  27. Jian-Kang D, Hong-Bo W, Yang-Yang L et al (2014) Virtual machine placement optimizing to improve network performance in cloud data centers. J China Univ Posts Telecommun 21(3):62–70

    Article  Google Scholar 

  28. Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, UK

    Google Scholar 

  29. Yang XS (2009) Firefly algorithms for multimodal optimization [C]//International Symposium on Stochastic Algorithms. Springer, Berlin Heidelberg, pp 169–178

  30. Yang X-S (2010) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, Frome

    Google Scholar 

  31. Yang XS, He X (2013) Firefly algorithm: recent advances and applications. Int J Swarm Intell 1(1):36–50. doi:10.1504/IJSI.2013.055801

    Article  Google Scholar 

  32. Fan X, Weber WD, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: Proceedings of 34th annual international symposium on computer architecture. New York, pp 13–23

  33. Hu L, Jin H, Liao X, et al. (2008) Magnet: a novel scheduling policy for power reduction in cluster with virtual machines. In: Proceedings of 2008 IEEE international conference on cluster computing. Tsukuba, pp 13–22

  34. Augkulanon P, Chai-ead N, Luangpaiboon P (2011) Bees and firefly algorithms for noisy nonlinear optimisation problems[A]. In: The international multiconference of engineers and computer scientists, p 2

  35. Marichelvam M K, Prabaharan T, Yang X-S (2014) A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems. IEEE Trans Evol Comput 18(2):301– 305

    Article  Google Scholar 

  36. Sayadi MK, Hafezalkotob A, Naini SGJ (2013) Firefly-inspired algorithm for discrete optimization problems: an application to manufacturing cell formation. J Manuf Syst 32(1):78– 84

    Article  Google Scholar 

  37. Lu K, Sun J (2016) Convergence analysis of firefly algorithm. J Front Comput Sci Technol 10(02)

  38. OpenStack, http://docs.openstack.org

  39. Jansen R, Brenner PR (2011) Energy efficient virtual machine allocation in the coud: an analysis of cloud allocation policies. In: Proceedings of international green computing conference and workshops (IGCC 2011), pp 1–8

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant NO. 61472139).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

    Weichao Ding, Chunhua Gu, Fei Luo, Yaohui Chang, Ulysse Rugwiro & Xiaoke Li

  2. School of Information Science and Engineering, Shanghai University of Electric Power, Shanghai, China

    Geng Wen

Authors
  1. Weichao Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunhua Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fei Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yaohui Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ulysse Rugwiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaoke Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Geng Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunhua Gu.

Additional information

This work is supported by the National Natural Science Foundation of China (Grant NO.61472139).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, W., Gu, C., Luo, F. et al. DFA-VMP: An efficient and secure virtual machine placement strategy under cloud environment. Peer-to-Peer Netw. Appl. 11, 318–333 (2018). https://doi.org/10.1007/s12083-016-0502-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-016-0502-z

Keywords

Search

Navigation