An adaptive power management scheme for Ethernet Passive Optical Networks

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An adaptive power management scheme for Ethernet Passive Optical Networks

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P. Sarigiannidis, K. Anastasiou, E. Karapistoli, V. Kakali, M. Louta, P. Angelidis: An adaptive power management scheme for Ethernet Passive Optical Networks. 2014.

Περίληψη

Undoubtedly, energy consumption in communication networks poses a significant threat to the environmental stability. Access networks contribute to this consumption by being composed of numerous energy inefficient devices and network equipment. Passive Optical Networks (PONs), one of the most promising candidates in the field of access networking, should avoid this bottleneck in the backhaul power consumption by lowering the energy use of the optical devices. In this paper, we move towards that direction by introducing an energy efficient power management scheme that encompasses two major goals: a) to reduce the energy consumption by allowing the optical devices to enter the sleep mode longer, and b) to concurrently maintain the network performance. To this end, we focus on the energy consumed by the optical network units (ONUs). The intelligence of the ONUs is stimulated by enhancing the decision making in determining the duration of the sleep period with learning from experience mechanism. Learning automata (LAs) are charged to address this challenge. The evaluation of the proposed enhanced power management scheme reveals considerable improvements in terms of energy savings, while at the same time the network performance remains in high levels. © 2014 IEEE.

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BibTeX (Download)

@conference{Sarigiannidis2014f,
title = {An adaptive power management scheme for Ethernet Passive Optical Networks},
author = { P. Sarigiannidis and K. Anastasiou and E. Karapistoli and V. Kakali and M. Louta and P. Angelidis},
url = {https://www.researchgate.net/publication/286812658_An_adaptive_power_management_scheme_for_Ethernet_Passive_Optical_Networks?_sg=9Zz2iY14mUeTZMc-phRI6wlWdfdweiaLCUVbz0BGxvUSnU_GRkZWSEFJrAzLqjctJX0s3ulybPAE6OQ},
doi = {10.1109/ISCC.2014.6912460},
year  = {2014},
date = {2014-01-01},
journal = {Proceedings - International Symposium on Computers and Communications},
abstract = {Undoubtedly, energy consumption in communication networks poses a significant threat to the environmental stability. Access networks contribute to this consumption by being composed of numerous energy inefficient devices and network equipment. Passive Optical Networks (PONs), one of the most promising candidates in the field of access networking, should avoid this bottleneck in the backhaul power consumption by lowering the energy use of the optical devices. In this paper, we move towards that direction by introducing an energy efficient power management scheme that encompasses two major goals: a) to reduce the energy consumption by allowing the optical devices to enter the sleep mode longer, and b) to concurrently maintain the network performance. To this end, we focus on the energy consumed by the optical network units (ONUs). The intelligence of the ONUs is stimulated by enhancing the decision making in determining the duration of the sleep period with learning from experience mechanism. Learning automata (LAs) are charged to address this challenge. The evaluation of the proposed enhanced power management scheme reveals considerable improvements in terms of energy savings, while at the same time the network performance remains in high levels. © 2014 IEEE.},
keywords = {bandwidth allocation, energy efficiency, Learning automata, Passive optical networks},
pubstate = {published},
tppubtype = {conference}
}
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