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An improved rough set approach for optimal trust measure parameter selection in cloud environments

  • Somu Nivethitha
  • M. R. Gauthama Raman
  • Obulaporam Gireesha
  • Krithivasan Kannan
  • V. S. Shankar SriramEmail author
Methodologies and Application
First Online:
  • 18 Downloads

Abstract

The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requirements. Further, the dynamic nature of the cloud ecosystem in terms of performance and new services increases the complexity of the cloud service selection problem. Trust-based service selection mechanisms which involve the intrinsic relations among the QoS parameters or trust measure parameters (TMPs) to evaluate the quality of the CSPs are the most preferred solution for the problem of cloud service selection. However, the accuracy of the trust-based service selection models and the CSP’s trust value relies on the optimality of the TMP subset obtained with respect to the service type. Hence, this work presents an efficient rough set theory-based hypergraph-binary fruit fly optimization (RST-HGBFFO), a cooperative bio-inspired technique to identify the optimal service-specific TMPs. Experiments on QWS dataset, Cloud Armor, and CISH—SASTRA trust feedback dataset reveal the predominance of RST-HGBFFO over the state-of-the-art feature selection techniques. The performance of RST-HGBFFO feature selection technique was validated using hypergraph-based computational model and WEKA tool in terms of reduct size, service ranking, classification accuracy, and time complexity.

Keywords

Trust measure parameters (TMPs) Rough set theory (RST) Hypergraph Binary fruit fly optimization (BFFO) Hypergraph-based computational model (HGCM) Cloud service ranking 
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Notes

Acknowledgements

This work was supported by The Department of Science and Technology – India, The Council for Scientific and Industrial Research – India, and TATA Realty – SASTRA Srinivasa Ramanujan Research Cell (Grant No: CSIR-SRF Fellowship/143345/2K17/1, SR/FST/MSI-107/2015, MRT/2017/000155, and SR/FST/ETI-349/2013).

Compliance with ethical standards

Conflict of interest

All the authors declare that they do not have any conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Glossary

CSP

Cloud service provider(s)

CU

Cloud user(s)

QoS

Quality of service

TMP

Trust measure parameter(s)

RST

Rough set theory

BFFO

Binary fruit fly optimization

RST-HGBFFO

Rough set theory-based hypergraph-binary fruit fly optimization

HGCM

Hypergraph-based computational model

XaaS

Anything as a service

MCDM

Multi-criteria decision making

FFOA

Fruit fly optimization algorithm

CSMIC-SMI

Cloud service measurement index consortium-service measurement index

SQR

Supervised quick reduct

QRR

Quick relative reduct

RST-HGBFFO

The proposed trust measure parameter selection technique

\( O = \left\{ {O_{1} , O_{2} , \ldots , O_{S} } \right\} \)

Observations

\( A = A_{1} , A_{2} , \ldots , A_{C} \)

Conditional attributes

D

Decisional attributes

\( Max_{Gen} \)

Maximum number of generations

\( PoP_{Size} \)

Population size

\( PoP_{Num} \)

Number of populations

Fitness

Fitness value

\( Best_{Smell} \)

Local best smell concentration

\( G_{Best_{Smell}} \)

Global best smell concentration

\( Best_{Pos} \)

Best position

References

  1. Abraham A, Falc R, Bello R (2009) Rough set theory: a true landmark in data analysis. Springer, BerlinCrossRefzbMATHGoogle Scholar
  2. Akshya Kaveri B, Gireesha O, Somu N et al (2017) E-FPROMETHEE: An Entropy Based Fuzzy Multi Criteria Decision Making Service Ranking Approach for Cloud Service Selection. In: Venkataramani G, Sankaranarayanan K, Mukherjee S, Arputharaj KSNS (eds) Smart Secure Systems—IoT and Analytics Perspective. ICIIT 2017 Communications in Computer and Information Science. Springer, Singapore, pp 224–238Google Scholar
  3. Al-Masri E, Mahmoud QH (2007) QoS-based discovery and ranking of Web services. In: Proceedings—international conference on computer communications and networks, ICCCN. pp 529–534Google Scholar
  4. Berge C (1973) Graphs and hypergraphs. North-Holland Pub, Co, AmsterdamzbMATHGoogle Scholar
  5. Chen Y, Miao D, Wang R (2010) A rough set approach to feature selection based on ant colony optimization. Pattern Recognit Lett 31:226–233.  https://doi.org/10.1016/J.PATREC.2009.10.013 CrossRefGoogle Scholar
  6. Chen Y, Zhu Q, Xu H (2015) Finding rough set reducts with fish swarm algorithm. Knowl-Based Syst 81:22–29.  https://doi.org/10.1016/J.KNOSYS.2015.02.002 CrossRefGoogle Scholar
  7. Chen Y, Zeng Z, Lu J (2017) Neighborhood rough set reduction with fish swarm algorithm. Soft Comput 21:6907–6918.  https://doi.org/10.1007/s00500-016-2393-6 CrossRefGoogle Scholar
  8. Dai H, Zhao G, Lu J, Dai S (2014) Comment and improvement on “a new Fruit Fly Optimization Algorithm: taking the financial distress model as an example”. Knowl-Based Syst 59:159–160.  https://doi.org/10.1016/j.knosys.2014.01.010 CrossRefGoogle Scholar
  9. Das AK, Sengupta S, Bhattacharyya S (2018) A group incremental feature selection for classification using rough set theory based genetic algorithm. Appl Soft Comput 65:400–411.  https://doi.org/10.1016/J.ASOC.2018.01.040 CrossRefGoogle Scholar
  10. Deogun JS, Choubey SK, Raghavan VV, Sever H (1998) Feature selection and effective classifiers. J Am Soc Inf Sci 49:423–434CrossRefGoogle Scholar
  11. Ding S, Xia C, Zhou K et al (2014a) Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation. PLoS ONE 9:1–11.  https://doi.org/10.1371/journal.pone.0097762 Google Scholar
  12. Ding S, Yang S, Zhang Y et al (2014b) Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems. Knowl-Based Syst 56:216–225CrossRefGoogle Scholar
  13. Ding S, Wang Z, Wu D, Olson DL (2017a) Utilizing customer satisfaction in ranking prediction for personalized cloud service selection. Decis Support Syst 93:1–10.  https://doi.org/10.1016/j.dss.2016.09.001 CrossRefGoogle Scholar
  14. Ding S, Xia C, Wang C et al (2017b) Multi-objective optimization based ranking prediction for cloud service recommendation. Decis Support Syst 101:106–114.  https://doi.org/10.1016/j.dss.2017.06.005 CrossRefGoogle Scholar
  15. El Aziz MA, Hassanien AE (2018) Modified cuckoo search algorithm with rough sets for feature selection. Neural Comput Appl 29:925–934.  https://doi.org/10.1007/s00521-016-2473-7 CrossRefGoogle Scholar
  16. Ganesan J, Inbarani HH, Azar AT, Polat K (2017) Tolerance rough set firefly-based quick reduct. Neural Comput Appl 28:2995–3008.  https://doi.org/10.1007/s00521-016-2514-2 CrossRefGoogle Scholar
  17. Garg SK, Versteeg S, Buyya R (2013) A framework for ranking of cloud computing services. Future Gener Comput Syst 29:1012–1023.  https://doi.org/10.1016/j.future.2012.06.006 CrossRefGoogle Scholar
  18. Gauthama Raman MR, Kirthivasan K, Shankar Sriram VS (2017a) Development of rough set—hypergraph technique for key feature identification in intrusion detection systems. Comput Electr Eng 59:189–200.  https://doi.org/10.1016/j.compeleceng.2017.01.006 CrossRefGoogle Scholar
  19. Gauthama Raman MR, Somu N, Kirthivasan K et al (2017b) An efficient intrusion detection system based on hypergraph—genetic algorithm for parameter optimization and feature selection in support vector machine. Knowl-Based Syst 134:1–12.  https://doi.org/10.1016/j.knosys.2017.07.005 CrossRefGoogle Scholar
  20. Gheyas I, Smith L (2010) Feature subset selection in large dimensionality domains. Pattern Recognit 43:5–13CrossRefzbMATHGoogle Scholar
  21. Hassanien AE, Gaber T, Mokhtar U, Hefny H (2017) An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Comput Electron Agric 136:86–96.  https://doi.org/10.1016/j.compag.2017.02.026 CrossRefGoogle Scholar
  22. Hu Z (2012) Decision rule induction for service sector using data mining—A rough set theory approach. The University of Texas at El Paso, El PasoGoogle Scholar
  23. 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.  https://doi.org/10.1016/j.cmpb.2013.10.007 CrossRefGoogle Scholar
  24. 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.  https://doi.org/10.1007/s00521-015-1840-0 CrossRefGoogle Scholar
  25. Jensen R, Shen Q (2003) Finding rough set reducts with ant colony optimization. In: Proceedings of the 2003 UK workshop on. pp 15–22Google Scholar
  26. Jensen R, Shen Q (2004) Fuzzy–rough attribute reduction with application to web categorization. Fuzzy Sets Syst 141:469–485MathSciNetCrossRefzbMATHGoogle Scholar
  27. Jiang F, Sui Y, Zhou L (2015) A relative decision entropy-based feature selection approach. Pattern Recognit 48:2151–2163.  https://doi.org/10.1016/j.patcog.2015.01.023 CrossRefzbMATHGoogle Scholar
  28. Jia-Yuarn Guo (2003) Rough set-based approach to data mining. Case Wester University, ClevelandGoogle Scholar
  29. Jothi G, Hannah Inbarani H (2016) Hybrid tolerance rough set-firefly based supervised feature selection for MRI brain tumor image classification. Appl Soft Comput 46:639–651.  https://doi.org/10.1016/j.asoc.2016.03.014 CrossRefGoogle Scholar
  30. Liao Y, Vemuri VR, Pasos A (2007) Adaptive anomaly detection with evolving connectionist systems. J Netw Comput Appl 30:60–80.  https://doi.org/10.1016/j.jnca.2005.08.005 CrossRefGoogle Scholar
  31. Liu Y, Esseghir M, Boulahia LM (2016) Evaluation of parameters importance in cloud service selection using rough sets. Creat Commons Attrib Int Licens (CC BY) 7:527–541Google Scholar
  32. Luan X-Y, Li Z-P, Liu T-Z (2016) A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm. Neurocomputing 174:522–529.  https://doi.org/10.1016/J.NEUCOM.2015.06.090 CrossRefGoogle Scholar
  33. Ma H, Hu Z, Li KZH (2016) Toward trustworthy cloud service selection: a time-aware approach using interval neutrosophic set. J Parallel Distrib Comput 96:75–94.  https://doi.org/10.1016/J.JPDC.2016.05.008 CrossRefGoogle Scholar
  34. Mao C, Lin R, Xu C, He Q (2017) Towards a trust prediction framework for cloud services based on PSO-driven neural network. IEEE Access 5:2187–2199.  https://doi.org/10.1109/ACCESS.2017.2654378 CrossRefGoogle Scholar
  35. Marudhadevi D, Dhatchayani VN, Sriram VSS (2014) A trust evaluation model for cloud computing using service level agreement. Comput J.  https://doi.org/10.1093/comjnl/bxu129 Google Scholar
  36. Mell P, Grance T (2011) The NIST definition of cloud computing. NIST Spec Publ 145:7.  https://doi.org/10.1136/emj.2010.096966 Google Scholar
  37. Min F, Liu Q (2009) A hierarchical model for test-cost-sensitive decision systems. Info Sci 179(14):2442–2452MathSciNetCrossRefzbMATHGoogle Scholar
  38. Moore D (1976) Chi square tests. Purdue Univ Lafayette Ind Dept of Statistics, West LafayettezbMATHGoogle Scholar
  39. Nina FRC (2007) On applications of rough sets theory to knowledge discovery. University of Puerto Rico, San JuanGoogle Scholar
  40. Obulaporam G, Somu N, ManiIyer Ramani GR, Boopathy AK, Vathula Sankaran SS (2018) GCRITICPA: A CRITIC and Grey relational analysis based service ranking approach for cloud service selection. In: Akoglu L, Ferrara E, Deivamani M, Baeza-Yates R, Yogesh P (eds) Advances in Data Science. ICIIT 2018. Communications in Computer and Information Science, vol 941. Springer, Singapore, pp 3–16Google Scholar
  41. Øhrn A (2001) Rosetta technical reference manual. Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, NorwayGoogle Scholar
  42. Pan W-T (2012) A new Fruit Fly Optimization Algorithm: taking the financial distress model as an example. Knowl-Based Syst 26:69–74.  https://doi.org/10.1016/j.knosys.2011.07.001 CrossRefGoogle Scholar
  43. Pawlak Z (1982a) Rough sets. Int J Comput Inf 99:1–51.  https://doi.org/10.1007/978-94-011-3534-4 zbMATHGoogle Scholar
  44. Pawlak Z (1982b) Rough sets. Int J Parallel Program 11:341–356.  https://doi.org/10.1007/978-94-011-3534-4 zbMATHGoogle Scholar
  45. Qu L (2016) Credible service selection in cloud environments. Macquarie University, SydneyGoogle Scholar
  46. Qu L, Wang Y, Orgun MA et al (2015) CCCloud: context-aware and credible cloud service selection based on subjective assessment and objective assessment. IEEE Trans Serv Comput 8:369–383.  https://doi.org/10.1109/TSC.2015.2413111 CrossRefGoogle Scholar
  47. Raman M, Kannan K, Pal S (2016) Rough set-hypergraph-based feature selection approach for intrusion detection systems. Def Sci 66:612CrossRefGoogle Scholar
  48. 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 92:89–97.  https://doi.org/10.1016/j.neunet.2017.01.012 CrossRefGoogle Scholar
  49. Raza MS, Qamar U (2018) Feature selection using rough set-based direct dependency calculation by avoiding the positive region. Int J Approx Reason 92:175–197.  https://doi.org/10.1016/J.IJAR.2017.10.012 MathSciNetCrossRefzbMATHGoogle Scholar
  50. Sheng M Cloud Armor. https://cs.adelaide.edu.au/~cloudarmor/home.html. Accessed 12 April 2018
  51. Shen L, Chen H, Yu Z et al (2016) Evolving support vector machines using fruit fly optimization for medical data classification. Knowl-Based Syst 96:61–75.  https://doi.org/10.1016/j.knosys.2016.01.002 CrossRefGoogle Scholar
  52. Somu N, Raman MRG, Kirthivasan K, Sriram VSS (2016) Hypergraph based feature selection technique for medical diagnosis. J Med Syst 40:239.  https://doi.org/10.1007/s10916-016-0600-8 CrossRefGoogle Scholar
  53. Somu N, Kirthivasan K, Shankar SS (2017a) A computational model for ranking cloud service providers using hypergraph based techniques. Future Gener Comput Syst 68:14–30.  https://doi.org/10.1016/j.future.2016.08.014 CrossRefGoogle Scholar
  54. Somu N, Kirthivasan K, Sriram VSS (2017b) A rough set-based hypergraph trust measure parameter selection technique for cloud service selection. J Supercomput 73:4535–4559.  https://doi.org/10.1007/s11227-017-2032-8 CrossRefGoogle Scholar
  55. Somu N, Gauthama Raman MR, Kalpana V et al (2018a) An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection. Neural Netw 108:339–354CrossRefGoogle Scholar
  56. Somu N, Gauthama Raman MR, Krithivasan K, Shankar Sriram VS (2018b) A trust centric optimal service ranking approach for cloud service selection. Futur Gener Comput Syst 86:234–252.  https://doi.org/10.1016/j.future.2018.04.033 CrossRefGoogle Scholar
  57. Sosinsky B (2010) Cloud Computing Bible. Wiley, HobokenCrossRefGoogle Scholar
  58. Su Y, Guo J (2017) A novel strategy for minimum attribute reduction based on rough set theory and fish swarm algorithm. Comput Intell Neurosci 2017:1–7.  https://doi.org/10.1155/2017/6573623 CrossRefGoogle Scholar
  59. Su K, Xiao B, Liu B et al (2017) TAP: a personalized trust-aware QoS prediction approach for web service recommendation. Knowl-Based Syst 115:55–65.  https://doi.org/10.1016/j.knosys.2016.09.033 CrossRefGoogle Scholar
  60. 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.  https://doi.org/10.1016/j.jnca.2014.07.019 CrossRefGoogle Scholar
  61. Sun L, Ma J, Zhang Y et al (2016) Cloud-FuSeR: fuzzy ontology and MCDM based cloud service selection. Future Gener Comput Syst 57:42–55.  https://doi.org/10.1016/j.future.2015.11.025 CrossRefGoogle Scholar
  62. Tang M, Dai X, Liu J, Chen J (2017) Towards a trust evaluation middleware for cloud service selection. Future Gener Comput Syst 74:302–312.  https://doi.org/10.1016/j.future.2016.01.009 CrossRefGoogle Scholar
  63. Thampi S, Bhargava PA (2013) Managing trust in cyberspace. CRC Press, Boca RatonCrossRefGoogle Scholar
  64. Udhaya Kumar S, Hannah Inbarani H (2017) PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task. Neural Comput Appl 28:3239–3258.  https://doi.org/10.1007/s00521-016-2236-5 CrossRefGoogle Scholar
  65. Valley CC (2011) Service Measurement Index Version 1.0. Silicon Valley, Moffett FieldGoogle Scholar
  66. Velayutham C, Thangavel K (2011) Unsupervised quick reduct algorithm using rough set theory. J Electron Sci Technol 9:193–201Google Scholar
  67. Wang X, Yang J, Teng X, Weijun Xia RJ (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recognit Lett 28:459–471.  https://doi.org/10.1016/j.patrec.2006.09.003 CrossRefGoogle Scholar
  68. Wang X, Cao J, Xiang Y (2015) Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing. J Syst Softw 100:195–210.  https://doi.org/10.1016/j.jss.2014.10.047 CrossRefGoogle Scholar
  69. Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, BurlingtonGoogle Scholar
  70. Yao Y (2004) A Partition Model of Granular Computing. In: Peters JF, Skowron A, Grzymała-Busse JW, Kostek B, Świniarski RWSMS (eds) Transactions on rough sets I. Springer, Berlin, pp 232–253CrossRefGoogle Scholar
  71. Zhong N, Dong J, Ohsuga S (2001) Using rough sets with heuristics for feature selection. J Intell Inf Syst 16:199–214CrossRefzbMATHGoogle Scholar
  72. Zouache D, Ben Abdelaziz F (2018) A cooperative swarm intelligence algorithm based on quantum-inspired and rough sets for feature selection. Comput Ind Eng 115:26–36.  https://doi.org/10.1016/J.CIE.2017.10.025 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Smart Energy Informatics Lab (SEIL), Department of Computer Science and EngineeringIndian Institute of TechnologyBombayIndia
  2. 2.iTrust, Centre for Research in Cyber SecuritySingapore University of Technology and Design (SUTD)SingaporeSingapore
  3. 3.Centre for Information Super Highway (CISH), School of ComputingSASTRA Deemed to be UniversityThanjavurIndia
  4. 4.Discrete Mathematics Research Laboratory (DMRL), School of Humanities and SciencesSASTRA Deemed to be UniversityThanjavurIndia

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