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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 achieving the same traffic rates as the energy-oblivious approaches. 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.
Datavetenskap (datalogi), Computer Sciences
Datavetenskap (datalogi), Computer Sciences
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 152 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |