Skip to main content
Log in

A Reliable Client Detection System during Load Balancing for Multi-tenant Cloud Environment

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Cloud computing is a broad-scale distributed computing system and is a widely accepted archetype that has many luxurious features, including traffic scalability, resource allocation, accessibility, inter-communication cost, and many more. Security is considered one of the primary concerns in the cloud. This paper chooses to formulate this problem by improving the Virtual Machine (VM) allocation policy. The authors have enhanced this policy from a different perspective by maintaining the difficulty in co-tenancy between attackers and targets. A secure resource allocation mechanism has been proposed to prevent multi-tenancy attacks, where attackers and the target are co-tenants on the same server. The multi-objective approach is implemented for a secure load balancing model called reliable client detection system (RCDS). This model inquires the safe or unsafe states all along VM distribution which accomplishes and estimates the reliability of the clients as per the historical performances. When cloud data centers have received the demands to deploy the upcoming jobs, the introduced model helps to find a secure physical machine for balancing the load with avoiding threats. It is evident from the results that RCDS can effectively diminish the risks and security score when increased from 100 to 1000 numbers of cloudlets under the safe states. Performance evaluation demonstrates that RCDS achieves high throughput, avoids traffic overflow, and reduces traffic up to 33.37% in the network.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

Data availability

Data derived from public domain resources.

References

  1. Alex ME, Kishore R. Forensics framework for cloud computing. Comput Electr Eng. 2017;60:193–205.

    Article  Google Scholar 

  2. Liu Y, Gong B, Xing C, Jian Y. A virtual machine migration strategy based on time series workload prediction using cloud model. Math Probl Eng. 2014;2014: 973069.

    Article  Google Scholar 

  3. Ristenpart T, Tromer E, Shacham H, Savage S. Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds. In: Proceedings of the 16th ACM conference on Computer and communications security, pp. 199–212, 2009.

  4. Hu K-H, Jianguo W, Tzeng G-H. Risk factor assessment improvement for China’s cloud computing auditing using a new hybrid MADM model. Int J Inform Technol Decis Mak. 2017;16(3):737–77.

    Article  Google Scholar 

  5. Zhang Y, Juels A, Reiter MK, Ristenpart T. Cross-VM side channels and their use to extract private keys. In: Proceedings of the 2012 ACM conference on Computer and communications security, pp. 305–316, 2012.

  6. Moon S-J, Sekar V, Reiter MK. Nomad: mitigating arbitrary cloud side channels via provider-assisted migration. In: Proceedings of the 22nd ACM sigsac conference on computer and communications security

  7. Sun Q, Shen Qi, Li C, Wu Z. Selance: secure load balancing of virtual machines in cloud. In: 2016 IEEE Trustcom/BigDataSE/ISPA, pp. 662–669. IEEE, 2016.

  8. Han Y, Chan J, Alpcan T, Leckie C. Using virtual machine allocation policies to defend against co-resident attacks in cloud computing. IEEE Trans Depend Secur Comput. 2015;14(1):95–108.

    Google Scholar 

  9. Duan J, Yang Yuanyuan. A load balancing and multi-tenancy oriented data center virtualization framework. IEEE Trans Parallel Distrib Syst. 2017;28(8):2131–44.

    Article  Google Scholar 

  10. Wang Z, Hayat MM, Ghani N, Shaban KB. Optimizing cloud-service performance: efficient resource provisioning via optimal workload allocation. IEEE Trans Parallel Distrib Syst. 2016;28(6):1689–702.

    Article  Google Scholar 

  11. Deng R, Lu R, Lai C, Luan TH, Liang H. Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J. 2016;3(6):1171–81.

    Google Scholar 

  12. Fernández-Cerero D, Jakóbik A, Grzonka D, Kołodziej J, Fernández-Montes A. Security supportive energy-aware scheduling and energy policies for cloud environments. J Parallel Distrib Comput. 2018;119:191–202.

    Article  Google Scholar 

  13. Singh AK, Kumar J. Secure and energy aware load balancing framework for cloud data centre networks. Electron Lett. 2019;55(9):540–1.

    Article  Google Scholar 

  14. Saxena D, Singh AK. OSC-MC: online secure communication model for cloud environment. IEEE Commun Lett. 2021;25:2844–8.

    Article  Google Scholar 

  15. Papagianni C, Leivadeas A, Papavassiliou S, Maglaris V, Cervello-Pastor C, Monje A. On the optimal allocation of virtual resources in cloud computing networks. IEEE Trans Comput. 2013;62(6):1060–71.

    Article  MathSciNet  MATH  Google Scholar 

  16. Zeng L, Veeravalli B, Li X. Saba: a security-aware and budget-aware workflow scheduling strategy in clouds. J Parallel Distrib Comput. 2015;75:141–51.

    Article  Google Scholar 

  17. Usmin S, Irudayaraja MA, Muthaiah U. Dynamic placement of virtualized resources for data centers in cloud. In: International Conference on Information Communication and Embedded Systems (ICICES2014), pp. 1–7, 2014.

  18. Cui L, Tso FP, Pezaros DP, Jia W. Plan: a policy-aware vm management scheme for cloud data centres. In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), pp. 142–151. IEEE, 2015.

  19. Liu Y, Lee MJ. Security-aware resource allocation for mobile cloud computing systems. In: 2015 24th International Conference on Computer Communication and Networks (ICCCN), pages 1–8, 2015.

  20. Li B, Liu P, Lin L. A cluster-based intrusion detection framework for monitoring the traffic of cloud environments. In: 2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud), pages 42–45. IEEE, 2016.

  21. Yassin M, Talhi C, Boucheneb H. Itadp: an inter-tenant attack detection and prevention framework for multi-tenant saas. J Inform Secur Appl. 2019;49: 102395.

    Google Scholar 

  22. Saxena D, Gupta I, Kumar J, Singh AK, Wen X. A secure and multiobjective virtual machine placement framework for cloud data center. IEEE Syst J. 2021;16:3163–74.

    Article  Google Scholar 

  23. AlJahdali H, Albatli A, Garraghan P, Townend P, Lau L, Xu J. Multi-tenancy in cloud computing. In: 2014 IEEE 8th international symposium on service oriented system engineering, pp. 344–351. IEEE, 2014.

  24. Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R. Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp. 2011;41(1):23–50.

    Article  Google Scholar 

  25. Xiao Z, Song Weijia, Chen Q. Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans Parallel Distrib Syst. 2012;24(6):1107–17.

    Article  Google Scholar 

  26. Zhao J, Yang K, Wei X, Ding Y, Liang H, Gaochao X. A heuristic clustering-based task deployment approach for load balancing using Bayes theorem in cloud environment. IEEE Trans Parallel Distrib Syst. 2015;27(2):305–16.

    Article  Google Scholar 

  27. Chhabra S, Singh AK. Dynamic hierarchical load balancing model for cloud data centre networks. Electron Lett. 2019;55(2):94–6.

    Article  Google Scholar 

  28. Ma T, Jiangxing W, Yuxiang H, Huang Wanwei. Optimal VM placement for traffic scalability using Markov chain in cloud data centre networks. Electron Lett. 2017;53(9):602–4.

    Article  Google Scholar 

  29. Lin C, Li G, Shan Z, Shi Y. Thinking and modeling for big data from the perspective of the I Ching. Int J Inform Technol Decis Mak. 2017;16(06):1451–63.

    Article  Google Scholar 

  30. Chhabra S, Singh AK. Dynamic data leakage detection model based approach for mapreduce computational security in cloud. In: 2016 Fifth International Conference on Eco-friendly Computing and Communication Systems (ICECCS), pp. 13–19. IEEE, 2016.

  31. Mi W, Qiu X, Zhang C. The analysis of security threats in structured p2p load balancing schemes. In: 2011 International Conference on Cloud and Service Computing, pp. 296–301. IEEE, 2011.

  32. Liu Y, Ruan X, Cai S, Li R, He H. An optimized vm allocation strategy to make a secure and energy-efficient cloud against co-residence attack. In: 2018 International Conference on Computing, Networking and Communications (ICNC), pp. 349–353. IEEE, 2018.

Download references

Author information

Authors and Affiliations

Authors

Contributions

All the authors have discussed and constructed the ideas, designed the security preserving model and wrote the paper together.

Corresponding author

Correspondence to Rishabh Gupta.

Ethics declarations

Conflict of Interest

The authors have no conflict of interest regarding the publication.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, A.K., Chhabra, S., Gupta, R. et al. A Reliable Client Detection System during Load Balancing for Multi-tenant Cloud Environment. SN COMPUT. SCI. 4, 86 (2023). https://doi.org/10.1007/s42979-022-01504-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01504-3

Keywords

Navigation