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Energy-Aware Traffic Engineering
EPFL.ORCID iD: 0000-0002-1256-1070
2010 (English)In: Proceedings of the 1st Int’l Conf. on Energy-Efficient Computing and Networking (E-ENERGY), Association for Computing Machinery (ACM), 2010, -178 p.Conference paper (Refereed)
Abstract [en]

Energy consumption of the Internet is already substantial and it is likely to increase as operators deploy faster equipment to handle popular bandwidth-intensive services, such as streaming and video-on-demand. Existing work on energy saving considers local adaptation relying primarily on hardware-based techniques, such as sleeping and rate adaptation. We argue that a complete solution requires a network-wide approach that works in conjunction with local measures. However, traditional traffic engineering objectives do not include energy. This paper presents Energy-Aware Traffic engineering (EATe), a technique that takes energy consumption into account while optimizing for low link utilization and high end-host sending rates. EATe uses a scalable, online technique to spread the load among multiple paths so as to increase energy savings. Our extensive ns-2 simulations over realistic topologies show that EATe succeeds in moving 21% of the links to the sleep state, while keeping the same sending rates and being close to the optimal energy-aware solution. Further, we demonstrate that EATe successfully handles changes in traffic load and quickly restores a low overall energy state. Alternatively, EATe can move links to lower energy levels, resulting in energy savings of 8%. Finally, EATe can succeed in making 16% of active routers sleep.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2010. -178 p.
National Category
Computer Science
URN: urn:nbn:se:kth:diva-147098OAI: diva2:727667
The 1st Int’l Conf. on Energy-Efficient Computing and Networking (E-ENERGY)March 13-15 2010, Passau, Germany

QC 20140704

Available from: 2014-06-23 Created: 2014-06-23 Last updated: 2014-07-04Bibliographically approved

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Kostic, Dejan
Computer Science

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