Adaptive state transition control for energy-efficient gigabit-capable passive optical networks

Adaptive state transition control for energy-efficient gigabit-capable passive optical networks In this paper, we investigate power management problems that affect gigabit-capable passive optical networks (GPONs). In GPONs, the basic principle for power reduction is to keep optical network units (ONUs) in the Power Saving state, wherein some of the hardware and software functions are turned off. Current research focuses on scheduling and determining the length of the sleep periods for ONUs that are in the Power Saving state. Our investigation indicates that keeping ONUs in the Power Saving state is not necessarily the most energy-efficient practice. The Power Saving state and the Full Power state must be jointly considered. Our study also reveals that traffic distribution is a critical factor. Considering only the average is insufficient. The variance of packet arrival also must be included when designing a green GPON. We have analyzed the power consumption in a GPON and determined the optimal load threshold for triggering a state transition from the Power Saving state to the Full Power state. For the reverse direction, we propose a neural network-based adaptive control scheme to achieve near optimal control of the transition from Full Power to Power Saving. We also propose a burst transmission scheme to determine the sleep period for an ONU in the Power Saving state. Unlike the proposal of ITU-T, which uses a fixed length for the sleep period, the state sojourn time in our approach is dynamically adjusted. We have carried out extensive simulations to evaluate the performance of the proposed scheme. Simulation results show that the total energy consumption of the proposed scheme is almost equal to the optimal control scheme. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Photonic Network Communications Springer Journals

Adaptive state transition control for energy-efficient gigabit-capable passive optical networks

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Publisher
Springer US
Copyright
Copyright © 2015 by Springer Science+Business Media New York
Subject
Computer Science; Computer Communication Networks; Electrical Engineering; Characterization and Evaluation of Materials
ISSN
1387-974X
eISSN
1572-8188
D.O.I.
10.1007/s11107-015-0503-5
Publisher site
See Article on Publisher Site

Abstract

In this paper, we investigate power management problems that affect gigabit-capable passive optical networks (GPONs). In GPONs, the basic principle for power reduction is to keep optical network units (ONUs) in the Power Saving state, wherein some of the hardware and software functions are turned off. Current research focuses on scheduling and determining the length of the sleep periods for ONUs that are in the Power Saving state. Our investigation indicates that keeping ONUs in the Power Saving state is not necessarily the most energy-efficient practice. The Power Saving state and the Full Power state must be jointly considered. Our study also reveals that traffic distribution is a critical factor. Considering only the average is insufficient. The variance of packet arrival also must be included when designing a green GPON. We have analyzed the power consumption in a GPON and determined the optimal load threshold for triggering a state transition from the Power Saving state to the Full Power state. For the reverse direction, we propose a neural network-based adaptive control scheme to achieve near optimal control of the transition from Full Power to Power Saving. We also propose a burst transmission scheme to determine the sleep period for an ONU in the Power Saving state. Unlike the proposal of ITU-T, which uses a fixed length for the sleep period, the state sojourn time in our approach is dynamically adjusted. We have carried out extensive simulations to evaluate the performance of the proposed scheme. Simulation results show that the total energy consumption of the proposed scheme is almost equal to the optimal control scheme.

Journal

Photonic Network CommunicationsSpringer Journals

Published: Apr 5, 2015

References

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