Skip to main content

Advertisement

Log in

Storage allocation scheme for virtual instances of cloud computing

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Cloud computing delivers resources and services through virtual machines on a pay-as-you-go basis. The allocation of storage space to users is usually determined by means of open allocation mechanisms that cannot guarantee an efficient allocation. Current allocation mechanisms do not consider user requests when making provisioning decisions. In other words, they assume that the storage spaces are fixed. In this study, we propose an algorithm for allocating storage spaces based on the requests of users. We present a unified storage allocation scheme (USAS) for cloud computing. USAS is a dynamic storage allocation framework for unlimited, limited, and free users. Our proposed approach is based on a storage partitioning policy, and we have compared our proposed scheme with open storage scheme and fixed storage scheme with common partition. We show through simulation study that USAS dynamically allocates space for different user requirements for all traffic loads.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Somasundaram T (2014) Govindarajan K CLOUDRB: a framework for scheduling and managing High-Performance Computing (HPC) applications in science cloud. Future Gener Comput Syst 34:47–65

    Article  Google Scholar 

  2. Malik S, Huet F, Caromel D (2014) Latency based group discovery algorithm for network aware cloud scheduling. Future Gener Comput Syst 31:28–39

    Article  Google Scholar 

  3. Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1(1):7–18

    Article  Google Scholar 

  4. Zaman S, Grosu D (2013) Combinatorial auction-based allocation of virtual machine instances in clouds. J Parallel Distrib Comput 73(4):495–508

    Article  Google Scholar 

  5. García AG, Espert IB, García VH (2014) SLA-driven dynamic cloud resource management. Future Gener Comput Syst 31:1–11

    Article  Google Scholar 

  6. Buyya R et al (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616

    Article  Google Scholar 

  7. Li Q et al (2009) Adaptive management of virtualized resources in cloud computing using feedback control. In: 2009 1st international conference on information science and engineering (ICISE). IEEE

  8. Kalyvianaki E, Charalambous T, Hand S (2009) Self-adaptive and self-configured CPU resource provisioning for virtualized servers using kalman filters. In: Proceedings of the 6th international conference on autonomic computing. ACM

  9. Zhu Q, Agrawal G (2010) Resource provisioning with budget constraints for adaptive applications in cloud environments. In: Proceedings of the 19th ACM international symposium on high performance distributed computing. ACM

  10. Padala P et al (2009) Automated control of multiple virtualized resources. In: Proceedings of the 4th ACM European conference on Computer systems. ACM

  11. Iqbal W et al (2011) Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Future Gener Comput Syst 27(6):871–879

    Article  Google Scholar 

  12. Huang D, He B, Miao C (2014) A survey of resource management in multi-tier web applications. Commun Surv Tutor IEEE 16(3):1574–1590

    Article  Google Scholar 

  13. Lu L et al (2014) Morpho: a decoupled MapReduce framework for elastic cloud computing. Future Gener Comput Syst 36:80–90

    Article  Google Scholar 

  14. Vasić N et al (2012) Dejavu: accelerating resource allocation in virtualized environments. In: ACM SIGARCH computer architecture news, vol. 40, No. 1. ACM

  15. Misra S et al (2014) Learning automata-based QoS framework for cloud IaaS. Netw Serv Manag IEEE Trans 11(1):15–24

    Article  Google Scholar 

  16. Zuo X, Zhang G, Tan W (2014) Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. Autom Sci Eng IEEE Trans 11(2):564–573

    Article  Google Scholar 

  17. Bu X, Rao J, Xu C-Z (2013) Coordinated self-configuration of virtual machines and appliances using a model-free learning approach. Parallel Distrib Syst IEEE Trans 24(4):681–690

    Article  Google Scholar 

  18. Xu C-Z, Rao J, Bu X (2012) URL: a unified reinforcement learning approach for autonomic cloud management. J Parallel Distrib Comput 72(2):95–105

    Article  Google Scholar 

  19. Garg SK et al (2014) SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J Netw Comput Appl 45:108–120

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Buyya R, Garg SK, Calheiros RN (2011) SLA-oriented resource provisioning for cloud computing: challenges, architecture, and solutions. 2011 International conference on cloud and service computing (CSC). IEEE

  22. AbuSharkh M et al (2013) Resource allocation in a network-based cloud computing environment: design challenges. Commun Mag IEEE 51(11):46–52

    Article  Google Scholar 

  23. Yeo CS et al (2010) Autonomic metered pricing for a utility computing service. Future Gener Comput Syst 26(8):1368–1380

    Article  Google Scholar 

  24. Samimi P, Teimouri Y, Mukhtar M (2014) A combinatorial double auction resource allocation model in cloud computing. Inf Sci. doi:10.1016/j.ins.2014.02.008

    Google Scholar 

  25. Skoutas DN, Makris P, Skianis C (2013) Optimized admission control scheme for coexisting femtocell, wireless and wireline networks. Telecommun Syst 53(3):357–371

    Article  Google Scholar 

Download references

Acknowledgments

This project was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research Project# 2015/01/3860.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manjur Kolhar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kolhar, M., Abd El-atty, S.M. & Rahmath, M. Storage allocation scheme for virtual instances of cloud computing. Neural Comput & Applic 28, 1397–1404 (2017). https://doi.org/10.1007/s00521-015-2173-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-015-2173-8

Keywords

Navigation