Cloud Storage Broker: a Novel Framework to Provide Cloud Storage Service Using Security Aware Optimal Resource Allocation

Rohitash Kumar Banyal; V. K. Jain; Pragya Jain

doi:10.15866/irecos.v10i9.7230

Cloud Storage Broker: a Novel Framework to Provide Cloud Storage Service Using Security Aware Optimal Resource Allocation

Rohitash Kumar Banyal^(1*), V. K. Jain⁽²⁾, Pragya Jain⁽³⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecos.v10i9.7230

Abstract

Motivated by most of the current cloud resource allocation policy which are cloud service provider/owner centric in which criteria are maximum utilization, maximum revenue collection, and performance, of cloud resources. In this paper we present a framework which act as cloud storage broker and provide the cloud storage service to user the using user centric criteria are which based on minimum cost and maximum security. Like other computing model resources allocation is also a challenging task in cloud computing. Cloud storage services are widely used to eliminate the need for local storage to lowering operational and maintenance costs. However, budget, security and privacy are major concerns when data are out-sourced. In this paper, we also describe and implement proposed cloud resource allocation framework that integrates cloud storage resources that are provided by different cloud service providers. Finally evaluate the proposed framework using case study in which different user request and cloud resource are generated randomly. The simulation results suggest that approach reach near to optimal solution for resource allocation. The result shows that efficiency of the framework and algorithm depends on the configuration of available resources as well as on configuration of user request.
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Keywords

Cloud Computing; Cloud Storage; Data Security; Cloud Resources Allocation; Storage Virtual Machines; Cloud Resource Broker; Cloud Resource Manager

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References

Mell, P; Grance, T. “The NIST Definition of cloud computing Version 15 Technical Report.” Computer and Information Sciences. 53(6),pp.1-10. 2009.
http://dx.doi.org/10.6028/nist.sp.800-145

Lee Badger, Robert Bohn, Shilong Chu, Mike Hogan, Fang Liu, Viktor Kaufmann, Jian. ”Useful Information for Cloud Adopters.” NIST Cloud Computing Program, 2(1), pp.1-73 , 2011.

Noemi Antedomenico. “Optimizing Security Of Cloud Computing Within The DOD.” Master’s thesis, Naval Postgraduate School, pp.1-107, 2010.

Shigeaki Tanimoto, Manami Hiramoto, Motoi Iwashita, Hiroyuki Sato, Atsushi Kanai. “Risk Management on the Security Problem in Cloud Computing.” First ACIS/JNU International Conference on Computers, Networks, Systems, and Industrial Engineering, 2011.
http://dx.doi.org/10.1109/cnsi.2011.82

Shilpashree Srinivasamurthy, David Q. Liu, ”Survey on Cloud Computing Security”, 2nd IEEE International Conference on Cloud Computing, 2010.

Gonzalez, Nelson, et al. "A quantitative analysis of current security concerns and solutions for cloud computing." Journal of Cloud Computing 1.1 (2012): 1-18.
http://dx.doi.org/10.1186/2192-113x-1-11

Armstrong, Django, et al. "Contextualization: dynamic configuration of virtual machines." Journal of Cloud Computing 4.1 (2015): 1-15.
http://dx.doi.org/10.1186/s13677-015-0042-8

Rezvani, M., Akbari, M. K., & Javadi, B. (2014). Resource Allocation in Cloud Computing Environments Based on Integer Linear Programming. The Computer Journal, bxu024
http://dx.doi.org/10.1093/comjnl/bxu024

Kumar, N., Chilamkurti, N., Zeadally, S., & Jeong, Y. S. (2013). Achieving Quality of Service (QoS) Using Resource Allocation and Adaptive Scheduling in Cloud Computing with Grid Support. The Computer Journal, bxt024.
http://dx.doi.org/10.1093/comjnl/bxt024

Buyya, R., & Murshed, M. (2002). A deadline and budget constrained cost-time optimisation algorithm for scheduling task farming applications on global grids. arXiv preprint cs/0203020.

Omara, F. A., Khattab, S. M., & Sahal, R. (2014). Optimum Resource Allocation of Database in Cloud Computing. Egyptian Informatics Journal, 15(1), 1-12.
http://dx.doi.org/10.1016/j.eij.2014.01.002

Wei, X., & Liu, H. (2015). A Cloud Manufacturing Resource Allocation Model Based on Ant Colony Optimization Algorithm. International Journal of Grid and Distributed Computing, 8(1), 55-66.

Garg, S. K., Versteeg, S., & Buyya, R. (2013). A framework for ranking of cloud computing services. Future Generation Computer Systems, 29(4), 1012-1023.
http://dx.doi.org/10.1016/j.future.2012.06.006

Liu, C. (2014). A Cloud-computing-based Resource Allocation Model for University Resource Optimization. International Journal of Grid and Distributed Computing, 7(3), 113-122.

Chang, F., Ren, J., & Viswanathan, R. (2010, July). Optimal resource allocation in clouds. In Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on (pp. 418-425). IEEE.
http://dx.doi.org/10.1109/cloud.2010.38

Chang, H. Y., Lu, H. C., Huang, Y. H., Lin, Y. W., & Tzang, Y. J. (2013). Novel auction mechanism with factor distribution rule for cloud resource allocation. The Computer Journal, bxt008.
http://dx.doi.org/10.1093/comjnl/bxt008

Lawrance, H., & Silas, S. (2013). Efficient Qos Based Resource Scheduling Using PAPRIKA Method for Cloud Computing. International Journal of Engineering Science and Technology (IJEST), 5(03).

Hadji, M., & Zeghlache, D. (2012, June). Minimum cost maximum flow algorithm for dynamic resource allocation in clouds. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on (pp. 876-882). IEEE.
http://dx.doi.org/10.1109/cloud.2012.36

Jovanovic, V., Njegus, A., The Use of GIS in Tourism Supply and WEB Portal Development, (2013) International Journal on Information Technology (IREIT), 1 (5), pp. 292-299.

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