Application of BOCR models in service oriented architecture (SOA): study on model validation through quantification for QoS considerations

Ravi Kumar Gedela (), K. Krishna Mohan () and V. Kamakshi Prasad
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Ravi Kumar Gedela: Jawaharlal Nehru Technological University Hyderabad
K. Krishna Mohan: University of Alberta
V. Kamakshi Prasad: Jawaharlal Nehru Technological University Hyderabad

International Journal of System Assurance Engineering and Management, 2018, vol. 9, issue 6, No 11, 1346-1354

Abstract: Abstract In the competitive market place, most organizations are going through a digital transformation to enhance the customer experience and reduce the risk, therefore quality of service (QoS), specifically performance, is becoming center stage in building real world applications. In building real world applications, two challenges are fundamental: firstly, how we effectively build models for performance under uncertainty, and secondly, in what way we validate the models through quantification. Consequently, our study reveals a significant research that has been underway and continue to be happening on the first part on model development, however, a limited research is in progress on QoS quantification in SOA applications. Hence, we attempted to fill the gap. Therefore, in this paper, we propose benefits–opportunities–cost–risk (BOCR) models for quantification of various QoS influencing factors using analytic network process (ANP) and analytic hierarchy process (AHP). We introduce an efficient algorithm for the first time based on BOCR–AHP/ANP methodology that validates the performance of real-world SOA applications. Most importantly, here in this paper, considered expert judgements and historical data from the in-the-field and on-the-project expertise to validate SOA–QoS making our proposed unique from other traditional practices. A case study has been carried out to validate the proposed approach and presented the results.

Keywords: QoS; Performance; ANP; AHP; BOCR; SOA; Model development; Prediction; Validation and quantification (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s13198-018-0751-8

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