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


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.


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



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.



Cloud service provider(s)


Cloud user(s)


Quality of service


Trust measure parameter(s)


Rough set theory


Binary fruit fly optimization


Rough set theory-based hypergraph-binary fruit fly optimization


Hypergraph-based computational model


Anything as a service


Multi-criteria decision making


Fruit fly optimization algorithm


Cloud service measurement index consortium-service measurement index


Supervised quick reduct


Quick relative reduct


The proposed trust measure parameter selection technique

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


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

Conditional attributes


Decisional attributes

\( Max_{Gen} \)

Maximum number of generations

\( PoP_{Size} \)

Population size

\( PoP_{Num} \)

Number of populations


Fitness value

\( Best_{Smell} \)

Local best smell concentration

\( G_{Best_{Smell}} \)

Global best smell concentration

\( Best_{Pos} \)

Best position


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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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