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

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.

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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).

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Correspondence to V. S. Shankar Sriram.

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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

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Nivethitha, S., Gauthama Raman, M.R., Gireesha, O. et al. An improved rough set approach for optimal trust measure parameter selection in cloud environments. Soft Comput 23, 11979–11999 (2019). https://doi.org/10.1007/s00500-018-03753-y

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Keywords

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