Hdfs federation là gì

FUTURE: 5478

Please cite this article as: F.M. Awaysheh, M. Alazab, M. Gupta et al., Next-generation big data federation access control: A reference model, Future Generation Computer

Systems [2020], //doi.org/10.1016/j.future.2020.02.052.

F.M. Awaysheh, M. Alazab, M. Gupta et al. / Future Generation Computer Systems xxx [xxxx] xxx 15

Appendix B 93

Algorithm 4: Performance relationship of two methods of

write benchmarking with R tool

1initialization;

2while

3m1= c[16.55, 33.9, 48.7, 65.9, 84.43]

4m2= c[21, 43, 61.8, 83.69, 107.2]

5m= c[m1,m2]

6do

7x= seq[100,500,100] x1= c[x,x] grupo=

as.factor[c[rep[’’A’’,5],rep[’’B’’,5]]] grupo=

relevel[grupo,ref="A"]

8postscript[’’graficoferas.eps’’] plot[x,m1,col=’’red’’,ylim=

c[0,130],xlim= c[100,500],xlab= ’’Size of the file [in

MB]’’, ylab= ’’Operation Time [in Seconds]’’,pch=15]

points[x,m2,col=’’blue’’,pch=17]

9legend[’’topleft’’,legend= c[’’Native WebHDFS’’,’’BDF

Access Broker’’],col=c[’’Red’’,’’Blue’’],lwd=1, lty=c[0,0],

pch=c[15,17],cex = 0.75]

10 abline[lm[m2 -1+x]] abline[lm[m1 -1+x]], dev.off[]

11 mod1= lm[m1 -1+x] mod2= lm[m2 -1+x]

12 mod= lm[m -1+x1+grupo]

13 end

94

References1

[1] D. Cutting, M. Cafarella, Apache Hadoop, 2006, //hadoop.apache.org.2

[Accessed 11 February 2020].3

[2] D. Borthakur, The Hadoop distributed file system: Architecture and design,4

Hadoop Proj. Website 11 [2007] [2007] 21.5

[3] HTCondor, Deploying high-throughput cluster using HTCondor over HDFS,6

HTCondor Manual, 2019, //research.cs.wisc.edu/htcondor/manual/v8.7

8/index.html. [Accessed 11 February 2020].8

[4] M. Zaharia, M. Chowdhury, M.J. Franklin, S. Shenker, I. Stoica, Spark: Cluster9

computing with working sets, HotCloud 10 [10–10] [2010] 95.10

[5] P. Colombo, E. Ferrari, Access control in the era of big data: State of the art11

and research directions, in: Proceedings of the 23nd ACM on Symposium12

on Access Control Models and Technologies, ACM, 2018, pp. 185–192.13

[6] Cloudera, Scaling namespaces and optimizing data storage, 2017,14

//docs.cloudera.com/HDPDocuments/HDP3/HDP-3.1.5/data-15

storage/content/scaling_a_cluster_using_hdfs_federation.html. [Accessed16

10 February 2020].17

[7] Apache Hadoop Project, YARN federation, 2016, //hadoop.apache.org/18

docs/current3/hadoop-yarn/hadoop- yarn-site/Federation.html. [Accessed19

13 June 2019].20

[8] K. Shvachko, H. Kuang, S. Radia, R. Chansler, The Hadoop distributed file21

system, in: Mass Storage Systems and Technologies [MSST], 2010 IEEE 26th22

Symposium on, Ieee, 2010, pp. 1–10.23

[9] Apache Knox, REST API And application gateway for the apache hadoop24

ecosystem, 2019, //knox.apache.org/. [Accessed 11 February 2020].25

[10] Apache Hadoop, Deploying Hadoop 3.x secure mode, 2019,26

//hadoop.apache.org/docs/current3/hadoop-project- dist/hadoop-27

common/SecureMode.html. [Accessed 11 February 2020].28

[11] Apache Hadoop, Authentication for, Hadoop 3.x HTTP web con-29

soles, 2019, //hadoop.apache.org/docs/current3/hadoop-project- dist/30

hadoop-common/HttpAuthentication.html. [Accessed 11 February 2020].31

[12] M. Gupta, F. Patwa, J. Benson, R. Sandhu, Multi-layer authorization32

framework for a representative Hadoop ecosystem deployment, in: Pro-33

ceedings of the 22nd ACM on Symposium on Access Control Models and34

Technologies, ACM, 2017, pp. 183–190.35

[13] Apache Atlas, Data governance and metadata framework for Hadoop, 2019,36

//atlas.apache.org. [Accessed 11 February 2020].37

[14] D. Smiley, E. Pugh, K. Parisa, M. Mitchell, Apache Solr Enterprise Search38

Server, Packt Publishing Ltd, 2015.39

[15] Knoldus, HDFS erasure coding, Hadoop 3.x, 2018, //blog.knoldus.com/40

hdfs-erasure- coding-hadoop- 3-0/. [Accessed 10 February 2020].41

[16] R.R. Parmar, S. Roy, D. Bhattacharyya, S.K. Bandyopadhyay, T.-H. Kim,42

Large-scale encryption in the hadoop environment: Challenges and43

solutions, IEEE Access 5 [2017] 7156–7163.44

[17] M. Gupta, F. Patwa, R. Sandhu, Object-tagged RBAC model for the Hadoop45

ecosystem, in: IFIP Annual Conference on Data and Applications Security46

and Privacy, Springer, 2017, pp. 63–81. 47

[18] M. Gupta, F. Patwa, R. Sandhu, An attribute-based access control model 48

for secure big data processing in Hadoop ecosystem, in: Proceedings of 49

the Third ACM Workshop on Attribute-Based Access Control, ACM, 2018, 50

pp. 13–24. 51

[19] P. Colombo, E. Ferrari, Enhancing MongoDB with purpose-based access 52

control, IEEE Trans. Dependable Secure Comput. 14 [6] [2017] 591–604. 53

[20] Intel, Big data: Securing intel it’s apache hadoop platform, 2016, 54

//www.intel.com/content/dam/www/public/us/en/documents/white- 55

papers/big-data- securing-intel- it-apache- hadoop-platform- paper.pdf.56

[Accessed 11 February 2020]. 57

[21] D. Das, O. O’Malley, S. Radia, K. Zhang, Adding Security to Apache Hadoop, 58

Hortonworks, IBM. 59

[22] O. O’Malley, K. Zhang, S. Radia, R. Marti, C. Harrell, Hadoop Security Design, 60

Yahoo, Inc. Tech. Rep. 61

[23] P.P. Sharma, C.P. Navdeti, Securing big data Hadoop: a review of security 62

issues, threats and solution, IJCSIT 5. 63

[24] P. Colombo, E. Ferrari, Privacy aware access control for big data: a research 64

roadmap, Big Data Res. 2 [4] [2015] 145–154. 65

[25] M. Gupta, R. Sandhu, The GURA_G administrative model for user and group 66

attribute assignment, in: Proc. of NSS, Springer, 2016, pp. 318–332. 67

[26] X. Jin, R. Krishnan, R. Sandhu, A unified attribute-based access control 68

model covering DAC, MAC and RBAC, in: IFIP Annual Conference on Data 69

and Applications Security and Privacy, Springer, 2012, pp. 41–55. 70

[27] M. Gupta, J. Benson, F. Patwa, R. Sandhu, Dynamic groups and attribute- 71

based access control for next-generation smart cars, in: Proceedings of the 72

Ninth ACM Conference on Data and Application Security and Privacy, ACM, 73

2019, pp. 61–72. 74

[28] H. Ulusoy, M. Kantarcioglu, E. Pattuk, K. Hamlen, Vigiles: Fine-grained 75

access control for MapReduce systems, in: Big Data [BigData Congress], 76

2014 IEEE International Congress on, IEEE, 2014, pp. 40–47. 77

[29] H. Ulusoy, P. Colombo, E. Ferrari, M. Kantarcioglu, E. Pattuk, GuardMR: 78

Fine-grained security policy enforcement for MapReduce systems, in: Proc. 79

of ACM ASIACCS, 2015, pp. 285–296. 80

[30] R. Lu, H. Zhu, X. Liu, J.K. Liu, J. Shao, Toward efficient and privacy- 81

preserving computing in big data era, IEEE Netw. 28 [4] [2014] 82

46–50. 83

[31] J. Soria-Comas, J. Domingo-Ferrer, Big data privacy: challenges to privacy 84

principles and models, Data Sci. Eng. 1 [1] [2016] 21–28. 85

[32] O. Tene, J. Polonetsky, Big data for all: Privacy and user control in the age 86

of analytics, Nw. J. Tech. Intell. Prop. 11 [2012] xxvii. 87

[33] F.M. Awaysheh, J.C. Cabaleiro, T.F. Pena, M. Alazab, Poster: A pluggable 88

authentication module for big data federation architecture, in: Proceedings 89

of the 24th ACM Symposium on Access Control Models and Technologies, 90

ACM, 2019, pp. 223–225. 91

[34] P. Colombo, E. Ferrari, Access control technologies for big data manage- 92

ment systems: literature review and future trends, Cybersecurity 2 [1] 93

[2019] 3. 94

[35] D. Kulkarni, A fine-grained access control model for key–value systems, in: 95

Proceedings of the Third ACM Conference on Data and Application Security 96

and Privacy, ACM, 2013, pp. 161–164. 97

[36] Y. Shalabi, E. Gudes, Cryptographically enforced role-based access control 98

for NoSQL distributed databases, in: IFIP Annual Conference on Data and 99

Applications Security and Privacy, Springer, 2017, pp. 3–19. 100

[37] R.S. Sandhu, E.J. Coyne, H.L. Feinstein, C.E. Youman, Role-based access 101

control models, Computer 29 [2] [1996] 38–47. 102

[38] A. Kayes, W. Rahayu, T. Dillon, E. Chang, J. Han, Context-aware access 103

control with imprecise context characterization for cloud-based data 104

resources, Future Gener. Comput. Syst. 93 [2019] 237–255. 105

[39] F. Awaysheh, J.C. Cabaleiro, T.F. Pena, M. Alazab, Big data security frame- 106

works meet the intelligent transportation systems trust challenges, in: 107

2019 18th IEEE International Conference on Trust, Security and Privacy 108

in Computing and Communications/13th IEEE International Conference on 109

Big Data Science and Engineering [TrustCom/BigDataSE], IEEE, 2019, pp. 110

807–813. 111

[40] Y. Zhou, D. Feng, Y. Hua, W. Xia, M. Fu, F. Huang, Y. Zhang, A similarity- 112

aware encrypted deduplication scheme with flexible access control in the 113

cloud, Future Gener. Comput. Syst. 84 [2018] 177–189. 114

[41] S. Fugkeaw, H. Sato, Scalable secure access control policy update for 115

outsourced big data, Future Gener. Comput. Syst. 79 [2018] 364–373. 116

[42] L. Qiu, F. Cai, G. Xu, Quantum digital signature for the access control of 117

sensitive data in the big data era, Future Gener. Comput. Syst. 86 [2018] 118

372–379. 119

[43] C.-S. Li, H. Franke, C. Parris, B. Abali, M. Kesavan, V. Chang, Composable 120

architecture for rack scale Big Data computing, Future Gener. Comput. Syst. 121

67 [2017] 180–193. 122

[44] A. Noury, M. Amini, An access and inference control model for time series 123

databases, Future Gener. Comput. Syst. 92 [2019] 93–108. 124

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