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Ant Colony Based Self-Adaptive Energy Saving Routing for Energy Efficient Internet
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- Authors
- Young-Min Kim, Eun-Jung Lee, Hea-Sook Park, Jun-Kyun Choi, Hong-Shik Park
- Issue Date
- 2012-07
- Citation
- Computer Networks : The International Journal of Telecommunications Networking, v.56, no.10, pp.2343-2354
- ISSN
- 1389-1286
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- According to recent research, the current Internet wastes energy due to an un-optimized network design, which does not consider the energy consumption of network elements such as routers and switches. Looking toward energy saving networks, a generalized problem called the energy consumption minimized network (EMN) had been proposed. However, due to the NP-completeness of this problem, it requires a considerable amount of time to obtain the solution, making it practically intractable for large-scale networks. In this paper, we re-formulate the NP-complete EMN problem into a simpler one using a newly defined concept called 'traffic centrality'. We then propose a new ant colony-based self-adaptive energy saving routing scheme, referred to as A-ESR, which exploits the ant colony optimization (ACO) method to make the Internet more energy efficient. The proposed A-ESR algorithm heuristically solves the re-formulated problem without any supervised control by allowing the incoming flows to be autonomously aggregated on specific heavily-loaded links and switching off the other lightly-loaded links. Additionally, the A-ESR algorithm adjusts the energy consumption by tuning the aggregation parameter 棺, which can dramatically reduce the energy consumption during nighttime hours (at the expense of tolerable network delay performance). Another promising capability of this algorithm is that it provides a high degree of self-organizing capabilities due to the amazing advantages of the swarm intelligence of artificial ants. The simulation results in real IP networks show that the proposed A-ESR algorithm performs better than previous algorithms in terms of its energy efficiency. The results also show that this efficiency can be adjusted by tuning 棺. © 2012 Elsevier B.V. All rights reserved.
- KSP Keywords
- Ant colony optimization(ACO), Energy efficiency, Energy saving, High degree, IP networks, NP-completeness, Network Delay, Network Design, Routing scheme, Self-adaptive, Swarm intelligence