Using genetic algorithms to optimise dynamic power saving in communication links subject to quality of service requirements

Abaci, H., Parr, G. ORCID: https://orcid.org/0000-0002-9365-9132, McClean, S., Moore, A. and Krug, L. (2016) Using genetic algorithms to optimise dynamic power saving in communication links subject to quality of service requirements. Sustainable Computing: Informatics and Systems, 10. pp. 1-19.

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Abstract

Network devices, meeting increasing workload demand, are not efficiently Power-Workload Proportionate and consume a considerable amount of power even when the workload (utilisation) is low. This work proposes a novel Slowing Mechanism (SM) that provides Power Workload Proportionality for a wired network equipment to reduce power consumption. The Slowing will be achieved by adjusting the Operational Rate (OPR) of components according to traffic load. To meet applications’ (VoIP, Data and Video) performance requirements, a Safety Gap (SG) is proposed in the Slowing Mechanism. Many parameters need to be carefully set for performance requirements within Slowing Mechanism. A Genetic Algorithm (GA) optimisation dynamically set to respond to the variable incoming traffic pattern determines these parameters. Thus, this work is a GA and the Slowing Mechanism integration to provide an insight into how GA optimisation can be employed in a network environment, and to optimise parameters in real-time. The results demonstrate that a considerable amount of saving is achievable. With the default hardware configuration, the SM optimises the parameters and offers a saving of over 60% for typical stable traffic, with acceptable packet delay and no packet loss. This saving is reduced to 17% saving for a bursty traffic pattern with acceptable performance degradation.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory Faculty of Science > Research Groups > Data Science and AI
Related URLs:	https://www.scopus.com/inward/record.uri...
Depositing User:	Pure Connector
Date Deposited:	24 Sep 2016 00:32
Last Modified:	10 Dec 2024 01:27
URI:	https://ueaeprints.uea.ac.uk/id/eprint/60083
DOI:	10.1016/j.suscom.2016.01.002

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