Entropy4Cloud: Using Entropy-Based Complexity To Optimize Cloud Service Resource Management Computational Intelligence for Cloud Computing
- Submitting institution
-
University of Hertfordshire
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13603886
- Type
- D - Journal article
- DOI
-
10.1109/TETCI.2017.2755691
- Title of journal
- IEEE Transactions on Emerging Topics in Computational Intelligence
- Article number
- -
- First page
- 13
- Volume
- 2
- Issue
- 1
- ISSN
- 2471-285X
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2018
- 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
-
2
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work is the first of its kind in terms that applies Leon Chua's Local Activity Theory to a complex cloud computing environment. The research contributed to a Knowledge Transfer Partnership (KTP) grant in collaboration with Advanced Collection Systems. During the project, the KTP associate, who was also registered as part-time PhD student with the University, produced a conference paper (https://doi.org/10.1109/WETICE.2017.58), which was invited as an extended journal paper (https://doi.org/10.1504/IJGUC.2019.102711). Both papers have been published with the KTP project’s industrial partner as a co-author (Martin Eaves from Advanced Collection Systems).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -