Combining Time Series Prediction Models Using Genetic Algorithm to Auto-scaling Web Applications Hosted in the Cloud Infrastructure
- Submitting institution
-
University of Derby
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 787028-1
- Type
- D - Journal article
- DOI
-
10.1007/s00521-015-2133-3
- Title of journal
- Neural Computing & Applications
- Article number
- -
- First page
- 2383
- Volume
- 27
- Issue
- -
- ISSN
- 1433-3058
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2015
- URL
-
https://link.springer.com/article/10.1007/s00521-015-2133-3
- 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
-
5
- Research group(s)
-
-
- Citation count
- 45
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper establishes and applies a methodology for analysis of the performance of 5 prediction models on extracts of real web server logs in a rigorous way enabling novel insights for operational predictions of server behaviours in hosted infrastructures. Citing works have used results in contexts such as could scaling but also load scaling in connected power systems.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
