Entropy4Cloud: Using Entropy-Based Complexity to Optimize Cloud Service Resource Management
- Submitting institution
-
The University of Kent
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9934
- 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
-
https://kar.kent.ac.uk/63355/
- 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 paper is significant because it is the first to apply Chua's Local Activity Theory to a complex cloud computing environment. Compared with the fair scheduler in Apache Spark, our proposed entropy scheduler is able to reduce overall cost by 23% and improve the average service response time by 15-20%, thereby significantly improving the performance of spark applications.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
