@article{Sarigiannidis2017c,

title = {An adaptive energy-efficient framework for time-constrained optical backbone networks},

author = { P. Sarigiannidis and V. Kakali and M. Fragakis},

url = {https://www.researchgate.net/publication/275219667_An_adaptive_energy-efficient_framework_for_time-constrained_optical_backbone_networks_AN_ADAPTIVE_ENERGY-EFFICIENT_FRAMEWORK},

doi = {10.1002/dac.2972},

year  = {2017},

date = {2017-01-01},

journal = {International Journal of Communication Systems},

volume = {30},

number = {3},

abstract = {Power management has emerged as a challenge of paramount importance having strong social and financial impact in the community. The rapid growth of information and communication technologies made backbone networks a serious energy consumer. Concurrently, backbone networking is deemed as one of the most promising areas to apply energy efficient frameworks. One of the most popular energy efficient techniques, in the context of backbone networks, is to intentionally switch off nodes and links that are monitored underutilized. Having in mind that optical technology has thoroughly dominated modern backbone networks, the function of switching off techniques entails fast operation and rigorous decision-making because of the tremendous speed of the underlying optical media. This paper addresses this challenge by introducing a novel, adaptive, and efficient power management scheme for large-scale backbone networks. The proposed framework exploits traffic patterns and dynamics in order to effectively switch off the set of network entities in a periodic fashion. An adaptive decision-making algorithm is presented to maximize the network energy gains with respect to time constraints as well as QoS guarantees. The conducted simulation results reveal considerable improvements when applying the proposed framework compared with other inflexible energy efficient schemes. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.},

keywords = {adaptive algorithms, backbone networks, energy efficiency, optical networks, power consumption},

pubstate = {published},

tppubtype = {article}

}

@article{Sarigiannidis2017d,

title = {Towards power consumption in optical networks: a cognitive prediction-based technique},

author = { P.G. Sarigiannidis and G. Papadimitriou and P. Nicopolitidis and E. Varvarigos},

url = {https://www.researchgate.net/publication/276509099_Towards_power_consumption_in_optical_networks_A_cognitive_prediction-based_technique},

doi = {10.1002/dac.2981},

year  = {2017},

date = {2017-01-01},

journal = {International Journal of Communication Systems},

volume = {30},

number = {7},

abstract = {Modern backbone, optical networks have developed into large, massive networks, which consist of numerous intermediate and terminal nodes as well as final users. These networks serve uninterruptedly multitude of users at the expense of considerable power consumption. Many research efforts are aimed at reducing energy consumption in large-scale optical networks, however, this objective is deemed laborious: the operation of such networks has to continuously remain in good levels without disruptions. One of the most compelling techniques to remedy this situation is to switch off redundant links and devices at specific (short) periods. These links and devices remain idle as long as the network can cope with the underlying traffic demands. Hence, a power mechanism is required to manage how and when underutilized network elements may be silent during network operation. Nevertheless, this management entails fast processing and efficient decision making. While many research efforts neglect this serious factor, the problem of reducing the power consumption still threats the development of today's backbone network. In this work, an effective, cognitive power management technique is proposed by enhancing the decision making with traffic prediction. Traffic capacity is estimated in each link within the network supporting, thus, more efficient decisions on switching off underutilized or even idle network elements a priori. The technique introduced succeeds high accuracy levels, while it offers energy savings up to 30% lower than other energy-aware schemes. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.},

keywords = {backbone networks, energy efficiency, optical networks, power consumption, prediction},

pubstate = {published},

tppubtype = {article}

}