A rough set-based hypergraph trust measure parameter selection technique for cloud service selection


Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom—a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking.

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  1. 1.

    Ghosh N, Ghosh S, Das S (2015) SelCSP: A framework to facilitate selection of cloud service providers. IEEE Trans Cloud 3(1):66–79. doi:10.1109/TCC.2014.2328578

  2. 2.

    Sosinsky B (2010) Cloud computing bible. Wiley, New York

    Book  Google Scholar 

  3. 3.

    Mell P, Grance T (2011) The NIST definition of cloud computing. NIST Spec Publ 145:7. doi:10.1136/emj.2010.096966

    Google Scholar 

  4. 4.

    Garg S, Versteeg S, Buyya R (2013) A framework for ranking of cloud computing services. Future Gener Comput Syst 29(4):1012–1023. doi:10.1016/j.future.2012.06.006

  5. 5.

    Ding S, Xia CY, Le Zhou K et al (2014) Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation. PLoS One. doi:10.1371/journal.pone.0097762

    Google Scholar 

  6. 6.

    Thampi S, Bhargava B, Atrey P (2013) Managing trust in cyberspace. Chapman and Hall/CRC

  7. 7.

    Ding S, Yang S, Zhang Y et al (2014) Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems. Knowl Based Syst 56:216–225. doi:10.1016/j.knosys.2013.11.014

  8. 8.

    Tang M, Dai X, Liu J, Chen J (2016) Towards a trust evaluation middleware for cloud service selection. Future Gener Comput Syst. doi:10.1016/j.future.2016.01.009

  9. 9.

    Noor TH, Sheng QZ, Yao L et al (2015) CloudArmor : supporting reputation-based trust management for cloud services. IEEE Trans Parallel Distrib Syst 27:367–380

    Article  Google Scholar 

  10. 10.

    Tang M, Dai X, Liu J, Chen J (2016) Towards a trust evaluation middleware for cloud service selection. Future Gener Comput Syst. doi:10.1016/j.future.2016.01.009

    Google Scholar 

  11. 11.

    Marudhadevi D, Dhatchayani VN, Sriram VSS (2014) A Trust evaluation model for cloud computing using service level agreement. doi:10.1093/comjnl/bxu129

    Google Scholar 

  12. 12.

    Qu L (2016) Credible service selection in cloud environments. Doctoral dissertation, Macquarie University

  13. 13.

    Liang H, Wang J, Yao Y (2007) User-oriented feature selection for machine learning. Comput J 50(4):421–434. doi:10.1093/comjnl/bxm012

  14. 14.

    Ben Saied Y, Olivereau A, Zeghlache D, Laurent M (2013) Trust management system design for the Internet of Things: a context-aware and multi-service approach. Comput Secur 39:351–365. doi:10.1016/j.cose.2013.09.001

    Article  Google Scholar 

  15. 15.

    Somu N, Raman MRG, Kirthivasan K, Sriram VSS (2016) Hypergraph based feature selection technique for medical diagnosis. J Med Syst 40:239. doi:10.1007/s10916-016-0600-8

    Article  Google Scholar 

  16. 16.

    CSMIC (2011) Cloud Service Measurement Index Consortium. “Service Measurement Index Version 1.0.”

  17. 17.

    Somu N, Kirthivasan K, Shankar SS (2017) A computational model for ranking cloud service providers using hypergraph based techniques. Future Gener Comput Syst 68:14–30. doi:10.1016/j.future.2016.08.014

    Article  Google Scholar 

  18. 18.

    Costa P (2013) Evaluating cloud services using multicriteria decision analysis M.S. Dissertation. Instituto Superior Técnico

  19. 19.

    IEEE Standards Association and Others (1998) IEEE STD 1061–1998, IEEE standard for a software quality metrics methodology

  20. 20.

    Cloud Armor project. http://cs.adelaide.edu.au/~cloudarmor/home.html. Accessed 15 Nov 2016

  21. 21.

    Moore D (1976) Chi-square tests

  22. 22.

    Øhrn A (2000) Rosetta technical reference manual. Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway

  23. 23.

    Somu N, Kirthivasan K, Sriram VSS (2016) A Computational model for ranking cloud service providers using hypergraph based techniques. Future Gener Comput Syst. doi:10.1016/j.future.2016.08.014

    Google Scholar 

  24. 24.

    Sun L, Dong H, Hussain FK et al (2014) Cloud service selection: state-of-the-art and future research directions. J Netw Comput Appl 45:134–150. doi:10.1016/j.jnca.2014.07.019

    Article  Google Scholar 

  25. 25.

    Sengupta N, Sen J, Sil J, Saha M (2013) Designing of on line intrusion detection system using rough set theory and Q-learning algorithm. Neurocomputing 111:161–168. doi:10.1016/j.neucom.2012.12.023

    Article  Google Scholar 

  26. 26.

    Swiniarski RW, Skowron A (2003) Rough set methods in feature selection and recognition. Pattern Recognit Lett 24:833–849. doi:10.1016/S0167-8655(02)00196-4

    Article  MATH  Google Scholar 

  27. 27.

    Jensen R, Shen Q (2007) Fuzzy-rough sets assisted attribute selection. IEEE Trans Fuzzy Syst 15(1):73–89. doi:10.1109/TFUZZ.2006.889761

  28. 28.

    Wang X, Yang J, Teng X, Xia W, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recognit Lett 28:459–471. doi:10.1016/j.patrec.2006.09.003

    Article  Google Scholar 

  29. 29.

    Jiang F, Sui Y, Zhou L (2015) A relative decision entropy-based feature selection approach. Pattern Recognit 48:2151–2163. doi:10.1016/j.patcog.2015.01.023

    Article  Google Scholar 

  30. 30.

    Inbarani HH, Bagyamathi M, Azar AT (2015) A novel hybrid feature selection method based on rough set and improved harmony search. Neural Comput Appl 26:1859–1880. doi:10.1007/s00521-015-1840-0

    Article  Google Scholar 

  31. 31.

    Inbarani HH, Azar AT, Jothi G (2014) Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis. Comput Methods Programs Biomed 113:175–185. doi:10.1016/j.cmpb.2013.10.007

    Article  Google Scholar 

  32. 32.

    Pawlak Z, Grzymala-Busse J, Slowinski R (1995) Rough sets. Communications 38(11):88–95. doi:10.1145/219717.219791

  33. 33.

    Pawlak Z, Skowron A (2007) Rudiments of rough sets. Inf Sci (Ny) 177(1):3–27. doi:10.1016/j.ins.2006.06.003

  34. 34.

    Gauthama Raman MR, Kirthivasan K, Sriram VSS (2017) Development of rough set-hypergraph technique for key feature identification in intrusion detection systems. Comput Electr Eng. doi:10.1016/j.compeleceng.2017.01.006

  35. 35.

    Mitra P, Murthy C, Pal S (2002) Unsupervised feature selection using feature similarity. IEEE Trans pattern Anal Mach Intell 24(3):301–312. doi:10.1109/34.990133

  36. 36.

    Chen H, Yang B, Liu J, Liu D (2011) A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl 38(7):9014–9022. doi:10.1016/j.eswa.2011.01.120

  37. 37.

    Abraham A, Falc R, Bello R (2009) Rough set theory: a true landmark in data analysis Rough set theory: a true landmark in data analysis, Vol 174. Springer Science & Business Media

  38. 38.

    Deo N (2016) Graph theory with applications to engineering and computer science. Courier Dover Publications

  39. 39.

    Berge C, Minieka E (1973) Graphs and hypergraphs, Vol 7. North-Holland publishing company, Amsterdam

  40. 40.

    Raman MRG, Somu N, Kirthivasan K, Sriram VSS (2017) A hypergraph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems. Neural Netw. doi:10.1016/j.neunet.2017.01.012

  41. 41.

    Kannan K, Kanna B, Aravindan C (2010) Root mean square filter for noisy images based on hyper graph model. Image Vis Comput

  42. 42.

    Bretto A, Gillibert L (2005) Hypergraph-based image representation. International Workshop on Graph-Based Representations in Pattern Recognition. Springer, Berlin, Heidelberg, pp 1–11. doi:10.1007/978-3-540-31988-7_1

  43. 43.

    Kavvadias DJ, Stavropoulos EC (2005) An efficient algorithm for the transversal hypergraph generation. J Graph Algorithms Appl 9:239–264

    MathSciNet  Article  MATH  Google Scholar 

  44. 44.

    Eiter T, Gottlob G (1995) Identifying the minimal transversals of a hypergraph and related problems. SIAM J Comput

  45. 45.

    Dhatchayani V, Sriram V (2014) Trust aware identity management for cloud computing. Int J Inf Commun Technol 6(3–4):369–380. doi:10.1504/IJICT.2014.063220

  46. 46.

    Hennan R, Roane J (2011) Security monitoring tool for computer network. US Pat. 7,904,456

  47. 47.

    Barth W (2008) Nagios: system and network monitoring. No Starch Press

  48. 48.

    Aceto G, Botta A, De Donato W, Pescapè A (2013) Cloud monitoring: a survey. Comput Netw 57:2093–2115. doi:10.1016/j.comnet.2013.04.001

    Google Scholar 

  49. 49.

    Deogun JS, Choubey SK, Raghavan VV, Sever H (1998) Feature selection and effective classifiers. J Am Soc Inf Sci 49:423–434

    Article  Google Scholar 

  50. 50.

    Hu Z (2012) Decision rule induction for service sector using data mining: a rough set theory approach M.S. Dissertation. The University of Texas At El Paso

  51. 51.

    Guo J-Y (2003) Rough set-based approach to data mining. IEEE, Los Alamitos

  52. 52.

    Jensen R, Shen Q (2003) Finding rough set reducts with ant colony optimization. In: Proceedings, 2003 UK Work, pp 15–22

  53. 53.

    Raman M, Kannan K, Pal S (2016) Rough set-hypergraph-based feature selection approach for intrusion detection systems. Def Sci J 66(6):612. doi:10.14429/dsj.66.10802

  54. 54.

    Nina F (2007) On applications of rough sets theory to knowledge discovery Doctoral dissertation, University of Puerto Rico Mayagüez Campus

  55. 55.

    Gheyas I, Smith L (2010) Feature subset selection in large dimensionality domains. Pattern Recognit 43(1):5–13. doi:10.1016/j.patcog.2009.06.009

  56. 56.

    Velayutham C, Thangavel K (2011) Unsupervised quick reduct algorithm using rough set theory. J Electron Sci Technol 9:193–201

    Google Scholar 

  57. 57.

    Chen Y, Zhu Q, Xu H (2015) Finding rough set reducts with fish swarm algorithm. Knowl Based Syst 81:22–29. doi:10.1016/j.knosys.2015.02.002

    Article  Google Scholar 

  58. 58.

    Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann

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The first and third author thanks the Department of Science and Technology, New Delhi, India, for INSPIRE Fellowship (Grant No: DST/INSPIRE Fellowship/2013/963) and Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions (SR/FST/ETI-349/2013) for their financial support. The second author thanks the Department of Science and Technology, New Delhi, India—Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions Government of India (SR/FST/MSI-107/2015) for their financial support.

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Corresponding author

Correspondence to V. S. Shankar Sriram.





Cloud service providers


Cloud users


Quality of service


Trust measure parameters


Rough set-based hypergraph technique


Hypergraph-based computational model



Something as a Service


Rough set theory


Supervised quick reduct


Quick relative reduct


Cloud Services Measurement Initiative Consortium–Service Measurement Index


Institute of Electrical and Electronics Engineers

TrustCom and RSHT for the identification of trustworthy cloud service providers


Cloud service registry and discovery


Trust computation engine


Service-level agreement


Identity management

Rough set-hypergraph-based trust measure parameter selection technique

\(D_T\) :

Decision table

\(S= \left\{ {S_1, S_2, \ldots , S_n }\right\} \) :

Samples in the decision table

\(\hbox {CA}= \left\{ {\hbox {CA}_1, \hbox {CA}_2, \ldots , \hbox {CA}_m }\right\} \) :

Set of conditional attributes

\(\hbox {DA}\) :

Decisional attribute

\(H \leftarrow \{\hbox {TMP}, \hbox {TMPR}^{\prime }\}\) :

Hypergraph constructed with TMPs as vertices and TMPR\(^{\prime }\) as hyperedges

\(TMP \leftarrow \{\hbox {TMP}_1, \hbox {TMP}_2, \ldots , \hbox {TMP}_n \}\) :

TMPs in a reduct

\(\hbox {TMPR}^{{\prime }}\leftarrow \left\{ {\hbox {TMPR}_1^{\prime }, \hbox {TMPR}_2^{\prime }, \ldots , \hbox {TMPR}_t^{\prime } }\right\} \) :

Reduct obtained from RST

\(\gamma \hbox {TMPR}^{{\prime }}\left( {\hbox {DA}}\right) \) :

The dependency of \(\hbox {TMPR}^{{\prime }}\) with \(\hbox {DA}\)

\(\gamma \hbox {CA}\left( {\hbox {DA}}\right) \) :

The dependency of \(\hbox {CA}\) with \(\hbox {DA}\)

\(H_T \left\{ {\hbox {TMP}}\right\} \) :

Sets that satisfy minimal transversal property of hypergraph

\(H_\mathrm{EXT} \left\{ {\hbox {TMP}}\right\} \) :

Sets that satisfy vertex linearity property of hypergraph

\(H_\mathrm{DIS} \left\{ {\hbox {TMP}}\right\} \) :

Sets that neither satisfy minimal transversal nor vertex linearity property

k :

Number of elements in \(\hbox {TMPR}_1^{\prime }\)

r :

Number of reducts

\(\hbox {TMP}_\mathrm{Opt}\) :

Optimal TMP subset


Key performance indicators

Experimental analysis

\(T_n\) :

Number of features

\(S_n\) :

Number of samples

\(H_n\) :

Number of elements in hyperedges

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Somu, N., Kirthivasan, K. & Shankar Sriram, V.S. A rough set-based hypergraph trust measure parameter selection technique for cloud service selection. J Supercomput 73, 4535–4559 (2017). https://doi.org/10.1007/s11227-017-2032-8

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  • Cloud service providers (CSPs)
  • Cloud users (CUs)
  • Trust measure parameters (TMPs)
  • Rough set theory (RST)
  • Hypergraph
  • Hypergraph-based computational model (HGCM)