Using genetic algorithms to optimise dynamic power saving in communication links subject to quality of service requirements
- Submitting institution
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The University of East Anglia
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 182620679
- Type
- D - Journal article
- DOI
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10.1016/j.suscom.2016.01.002
- Title of journal
- Sustainable Computing: Informatics and Systems
- Article number
- -
- First page
- 1
- Volume
- 10
- Issue
- -
- ISSN
- 2210-5379
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2016
- URL
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964822974&partnerID=40&md5=c7137c617ac4409a16cd281cea982096
- 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
-
4
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research was funded by an EPSRC-BT iCASE Award. It developed a novel Genetic Algorithm (GA) and Slowing Mechanism (SM) integration to provide insight of how a GA optimisation can be employed in a network environment to optimise parameters according to a set of real energy consumption data from BT Research Labs at Adastral. A 61% power saving was achieved by a 4.5 ms 97.5th percentile packet delay without packet loss for the on-site 30% utilised typical metro-core links with the given typical stable traffic. This work fed into the implementation of ICT energy efficient policies at BT Adastral Park.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -