Self-adaptive and online QoS modeling for cloud-based software services
- Submitting institution
-
The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 31795093
- Type
- D - Journal article
- DOI
-
10.1109/TSE.2016.2608826
- Title of journal
- IEEE Transactions on Software Engineering
- Article number
- -
- First page
- 453
- Volume
- 43
- Issue
- 5
- ISSN
- 0098-5589
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 19
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The first to develop a fully dynamic and self-adaptive Quality of Services(QoS) model and architecture for cloud-based services. This is significant as existing models drastically fail in calibrating environmental uncertainties/dynamism in their solution. It is distinctive and novel in leveraging information theory and machine learning techniques for its solution and in using datasets of industrial scale.
It informed a roadmapping agenda on Dynamic and Adaptive Search-Based Software Engineering (EPSRC Programme) with a theme on cloud. Related contributions received successive invitations to world-leading conferences (e.g.
SEAMS@ICSE), invited talks/seminars. Among the most downloaded articles for TSE, a world-leading Software Engineering journal, CORE-A*.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
