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Towards personalised and adaptive QoS assessments via context awareness
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Barakat, L., Taylor, Phillip M., Griffiths, Nathan, Taweel, A., Lucas, M. and Miles, S. (2018) Towards personalised and adaptive QoS assessments via context awareness. Computational Intelligence, 34 (2). pp. 468-494. doi:10.1111/coin.12129
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WRAP-towards-personalised-adaptive-QoS-assessments-Griffiths-2017.pdf - Accepted Version - Requires a PDF viewer. Download (2216Kb) | Preview |
Official URL: http://doi.org/10.1111/coin.12129
Abstract
QoS (quality of service) properties play an important role in distinguishing between functionally-equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, utilising the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been utilised to a limited extent by existing approaches. In response, we propose a context-aware quality learning model, realised via a learning-enabled service agent, exploiting the contextual characteristics of the domain in order to provide more personalised, accurate and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Quality of service (Computer networks) , Computational intelligence, Context-aware computing | ||||||||
| Journal or Publication Title: | Computational Intelligence | ||||||||
| Publisher: | Wiley-Blackwell Publishing, Inc. | ||||||||
| ISSN: | 0824-7935 | ||||||||
| Official Date: | May 2018 | ||||||||
| Dates: |
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| Date of first compliant deposit: | 18 April 2017 | ||||||||
| Volume: | 34 | ||||||||
| Number: | 2 | ||||||||
| Page Range: | pp. 468-494 | ||||||||
| DOI: | 10.1111/coin.12129 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Related URLs: |
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