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Abstract

As a typical distributed network computing environment, cloud computing has been growing tremendously recently. Although it could provide a convenient environment for the execution of data-intensive application, the primary challenge for it is how to choose the security date replica for data-intensive application from different storage resources. Considering that the security has the characteristic of fuzziness and randomness, it is quantified using the cloud model in this paper. On this basis, taking the constraints of time, cost, and security into account, a scheduling algorithm of data-intensive application based on weighted set covering problem (WSCP) is proposed. Simulation results demonstrate that the security-aware data replica selection strategy performs better than traditional selection strategy on time, cost, security utility value and load balancing deviation (LBD).

Keywords

Cloud computing Bag-of-Task application cloud model data replica selection security utility value

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References

[1]

I.M.

Abbadi and

Ruan, Towards trustworthy resource scheduling in clouds, IEEE Transactions on Information Forensics and Security 8(6) (2013), 973–984.

[2]

Aguiar,

Zhang and

Blanton, An overview of issues and recent developments in cloud computing and storage security, in: High Performance Cloud Auditing and Applications, Springer, New York, 2014, pp. 3–33.

[3]

H.H.E.

Al-Mistarihi and

C.H.

Yong, On fairness, optimizing replica selection in data grids, Parallel and Distributed Systems, IEEE Transactions on 20(8) (2009), 1102–1111.

[4]

R.M.

Almuttairi,

Wankar,

Negi and

C.R.

Rao, Smart replica selection for data grids using rough set approximations (RSDG), in: 2010 International Conference on Computational Intelligence and Communication Networks (CICN), IEEE, 2010, pp. 466–471.

[5]

Alrifai and

Risse, Combining global optimization with local selection for efficient QoS-aware service composition, in: Proceedings of the 18th International Conference on World Wide Web, ACM, 2009, pp. 881–890.

[6]

Bhatia,

S.K.

Muttoo and

M.P.S.

Bhatia, Secure requirement prioritized grid scheduling model, International Journal Network Security 15(6) (2013), 478–483.

[7]

Borthakur, The hadoop distributed file system: Architecture and design, Hadoop Project Website, 11, 21, 2007.

[8]

Buyya,

C.S.

Yeo,

Venugopal,

Broberg and

Brandic, Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer Systems 25(6) (2009), 599–616.

[9]Cloudsim, The Clouds Lab, (2012). Cloudsim: A framework for modeling and simulation of cloud computing infrastructures and services, available at: http://www.cloudbus.org/cloudsim/, accessed 28/02/2012.

10.

[10]

Fard,

Prodan and

Fahringer, A truthful dynamic workflow scheduling mechanism for commercial multicloud environments, IEEE Transactions on Parallel and Distributed Systems 24(6) (2013), 1203–1212.

11.

[11]

R.K.

Grace and

Manimegalai, Dynamic replica placement and selection strategies in data grids – A comprehensive survey, Journal of Parallel and Distributed Computing 74(2) (2014), 2099–2108.

12.

[12]

Hu and

Veeravalli, Requirement-aware scheduling of bag-of-tasks applications on grids with dynamic resilience, Computers, IEEE 62(10) (2013), 2108–2114.

13.

[13]

O.A.

Jadaan,

Abdulal,

M.A.

Hameed and

Jabas, Enhancing data selection using genetic algorithm, in: 2010 International Conference on Computational Intelligence and Communication Networks (CICN), IEEE, 2010, pp. 434–439.

14.

[14]

Li,

Liu and

Gan, A new cognitive model: Cloud model, International Journal of Intelligent Systems 24(3) (2009), 357–375.

15.

[15]

Mansouri,

G.H.

Dastghaibyfard and

Mansouri, Combination of data replication and scheduling algorithm for improving data availability in data grids, Journal of Network and Computer Applications 36(2) (2013), 711–722.

16.

[16]

Palmieri, GMPLS-based service differentiation for scalable QoS support in all-optical grid applications, Future Generation Computer Systems 22(6) (2006), 688–698.

17.

[17]

Palmieri, Percolation-based replica discovery in peer-to-peer grid infrastructures, in: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, Vol. 25, 2010, pp. 45–56.

18.

[18]

Palmieri,

Buonanno,

Venticinque,

Aversa and

Di Martino, A distributed scheduling framework based on selfish autonomous agents for federated cloud environments, Future Generation Computer Systems 29(6) (2013), 1461–1472.

19.

[19]

Palmieri and

Pardi, Network-aware replica optimization in the SCoPE grid infrastructure, in: Lecture Notes in Computer Science, Vol. 5073, 2008, pp. 845–857.

20.

[20]

Pandey and

Buyya, Scheduling workflow applications based on multi-source parallel data retrieval in distributed computing networks, The Computer Journal 55(11) (2012), 1288–1308.

21.

[21]

R.M.

Rahman,

Barker and

Alhajj, A predictive technique for replica selection in grid environment, in: Seventh IEEE International Symposium on Cluster Computing and the Grid, 2007. CCGRID 2007, IEEE, 2007, pp. 163–170.

22.

[22]

Rong,

S.T.

Nguyen and

M.G.

Jaatun, Beyond lightning: A survey on security challenges in cloud computing, Computers and Electrical Engineering 39(1) (2013), 47–54.

23.

[23]

Ryan, Cloud computing security: The scientific challenge, and a survey of solutions, Journal of Systems and Software 86(9) (2013), 2263–2268.

24.

[24]

Sashi and

A.S.

Thanamani, Dynamic replication in a data grid using a modified BHR region based algorithm, Future Generation Computer Systems 27(2) (2011), 202–210.

25.

[25]

Tan,

Sun,

Li et al., A trust service-oriented scheduling model for workflow applications in cloud computing, IEEE Systems Journal 8(3) (2014), 868–878.

26.

[26]

Toosi,

Calheiros and

Buyya, Interconnected cloud computing environments: Challenges, taxonomy, and survey, ACM Computing Surveys (CSUR) 47(1) (2014), 7.

27.

[27]

Venugopal and

Buyya, An SCP-based heuristic approach for scheduling distributed cloud workflow application on global grids, Journal of Parallel and Distributed Computing 68(4) (2008), 471–487.

28.

[28]

Viriyasitavat and

Martin, A survey of trust in workflows and relevant contexts, Communications Surveys & Tutorials, IEEE 14(3) (2012), 911–940.

29.

[29]

Wang,

Ramamohanarao and

Chen, Trust based robust scheduling and runtime adaptation of scientific workflow, Concurrency and Computation: Practice and Experience 21(16) (2009), 1982–1998.

30.

[30]

Wang,

Zhang,

Wang and

Qiu, A cloud-based trust model for evaluating quality of web services, Journal of Computer Science and Technology 25(6) (2010), 1130–1142.

31.

[31]

Wei,

Zhu,

Cao et al., Security and privacy for storage and computation in cloud computing, Information Sciences 258 (2014), 371–386.

32.

[32]

Yang,

Yan and

Zeng, How to handle uncertainties in AHP: The cloud Delphi hierarchical analysis, Information Sciences 222 (2013), 384–404.

33.

[33]

Yang,

Peng and

Fu, A framework of cloud service selection based on trust mechanism, International Journal of Ad Hoc and Ubiquitous Computing, to appear.