2022
Dimitrios Pliatsios; Thomas Lagkas; Vasileios Argyriou; Antonios Sarigiannidis; Dimitrios Margounakis; Theocharis Saoulidis; Panagiotis Sarigiannidis
A Hybrid RF-FSO Offloading Scheme for Autonomous Industrial Internet of Things Conference
IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2022, ISBN: 978-1-6654-0926-1.
Abstract | BibTeX | Tags: Computation offloading, energy efficiency, Free-space Optical Communications, Industrial Internet of Things, Multi-access Edge Computing | Links:
@conference{9798011,
title = {A Hybrid RF-FSO Offloading Scheme for Autonomous Industrial Internet of Things},
author = { Dimitrios Pliatsios and Thomas Lagkas and Vasileios Argyriou and Antonios Sarigiannidis and Dimitrios Margounakis and Theocharis Saoulidis and Panagiotis Sarigiannidis},
doi = {10.1109/INFOCOMWKSHPS54753.2022.9798011},
isbn = {978-1-6654-0926-1},
year = {2022},
date = {2022-01-01},
booktitle = {IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)},
pages = {1-6},
abstract = {The ever increasing demand for bandwidth triggered by data-intensive applications is imposing a considerable burden on the radio-frequency (RF) spectrum. A promising solution to address the spectrum congestion problem is the adoption of free-space optical (FSO) communications. In this work, we consider a hybrid RF-FSO system that enables the task offloading process from Industrial Internet-of-Things devices to a multi-access edge computing (MEC)-enabled base station (BS). We propose a solution that minimizes the total energy consumption of the system by deciding whether the RF or FSO link will be used for the task offloading and optimally allocating the device transmission power while taking into account the task requirements in terms of delay. The proposed solution is based on a decomposition-driven algorithm that employs integer linear programming (ILP) and Lagrange dual decomposition. Finally, we carry out system-level Monte Carlo simulations to evaluate the performance of the solution. The simulation results show that the proposed solution can minimize the total energy consumption within a few iterations, while also considering the respective latency requirements.},
keywords = {Computation offloading, energy efficiency, Free-space Optical Communications, Industrial Internet of Things, Multi-access Edge Computing},
pubstate = {published},
tppubtype = {conference}
}
The ever increasing demand for bandwidth triggered by data-intensive applications is imposing a considerable burden on the radio-frequency (RF) spectrum. A promising solution to address the spectrum congestion problem is the adoption of free-space optical (FSO) communications. In this work, we consider a hybrid RF-FSO system that enables the task offloading process from Industrial Internet-of-Things devices to a multi-access edge computing (MEC)-enabled base station (BS). We propose a solution that minimizes the total energy consumption of the system by deciding whether the RF or FSO link will be used for the task offloading and optimally allocating the device transmission power while taking into account the task requirements in terms of delay. The proposed solution is based on a decomposition-driven algorithm that employs integer linear programming (ILP) and Lagrange dual decomposition. Finally, we carry out system-level Monte Carlo simulations to evaluate the performance of the solution. The simulation results show that the proposed solution can minimize the total energy consumption within a few iterations, while also considering the respective latency requirements.
2021
T. Lagkas; D. Klonidis; P. Sarigiannidis; I. Tomkos
Optimized Joint Allocation of Radio, Optical, and MEC Resources for the 5G and Beyond Fronthaul Journal Article
In: IEEE Transactions on Network and Service Management, 2021.
Abstract | BibTeX | Tags: 5G and beyond, energy efficiency, joint resource allocation, Learning automata, MEC, New Radio, NFV | Links:
@article{Lagkas2021,
title = {Optimized Joint Allocation of Radio, Optical, and MEC Resources for the 5G and Beyond Fronthaul},
author = {T. Lagkas and D. Klonidis and P. Sarigiannidis and I. Tomkos},
url = {https://www.researchgate.net/publication/353062079_Optimized_Joint_Allocation_of_Radio_Optical_and_MEC_Resources_for_the_5G_and_Beyond_Fronthaul},
doi = {10.1109/TNSM.2021.3094789},
year = {2021},
date = {2021-07-06},
journal = {IEEE Transactions on Network and Service Management},
abstract = {In 5G and beyond telecommunication infrastructures a crucial challenge in achieving the strict Key Performance Indicators (KPIs) regarding capacity, latency, and guaranteed quality of service, is the efficient handling of the fronthaul bottleneck. This part of the next generation networks is expected to comprise the New Radio (NR) access and the Next Generation Passive Optical Network (NGPON) domains. Latest developments load the fronthaul with computing tasks as well (e.g., for AI-based processes) in the context of Mobile Edge Computing (MEC). Towards efficient management of all resource types, this paper proposes a joint allocation scheme with three optimization phases for radio, optical, and MEC resources. This scheme, which has been developed in the context of the blueSPACE 5G Infrastructure Public Private Partnership (5G PPP) project, exploits cutting-edge technologies, such as radio beamforming, spatial-spectral granularity in optical networks, and Network Function Virtualization (NFV), to provide dynamic, adaptive, and energy efficient allocation of resources. The devised model is mathematically described and the overall solution is evaluated in a realistic simulation scenario, demonstrating its effectiveness.},
keywords = {5G and beyond, energy efficiency, joint resource allocation, Learning automata, MEC, New Radio, NFV},
pubstate = {published},
tppubtype = {article}
}
In 5G and beyond telecommunication infrastructures a crucial challenge in achieving the strict Key Performance Indicators (KPIs) regarding capacity, latency, and guaranteed quality of service, is the efficient handling of the fronthaul bottleneck. This part of the next generation networks is expected to comprise the New Radio (NR) access and the Next Generation Passive Optical Network (NGPON) domains. Latest developments load the fronthaul with computing tasks as well (e.g., for AI-based processes) in the context of Mobile Edge Computing (MEC). Towards efficient management of all resource types, this paper proposes a joint allocation scheme with three optimization phases for radio, optical, and MEC resources. This scheme, which has been developed in the context of the blueSPACE 5G Infrastructure Public Private Partnership (5G PPP) project, exploits cutting-edge technologies, such as radio beamforming, spatial-spectral granularity in optical networks, and Network Function Virtualization (NFV), to provide dynamic, adaptive, and energy efficient allocation of resources. The devised model is mathematically described and the overall solution is evaluated in a realistic simulation scenario, demonstrating its effectiveness.
2017
P. Sarigiannidis; V. Kakali; M. Fragakis
An adaptive energy-efficient framework for time-constrained optical backbone networks Journal Article
In: International Journal of Communication Systems, vol. 30, no. 3, 2017.
Abstract | BibTeX | Tags: adaptive algorithms, backbone networks, energy efficiency, optical networks, power consumption | Links:
@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}
}
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.
P.G. Sarigiannidis; G. Papadimitriou; P. Nicopolitidis; E. Varvarigos
Towards power consumption in optical networks: a cognitive prediction-based technique Journal Article
In: International Journal of Communication Systems, vol. 30, no. 7, 2017.
Abstract | BibTeX | Tags: backbone networks, energy efficiency, optical networks, power consumption, prediction | Links:
@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}
}
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.
2014
P. Sarigiannidis; A. Gkaliouris; V. Kakali; M. Louta; G. Papadimitriou; P. Nicopolitidis; M. Obaidat
On forecasting the ONU sleep period in XG-PON systems using exponential smoothing techniques Conference
2014.
Abstract | BibTeX | Tags: bandwidth allocation, energy efficiency, exponential smoothing, Passive optical networks, XG-PON | Links:
@conference{Sarigiannidis20142580,
title = {On forecasting the ONU sleep period in XG-PON systems using exponential smoothing techniques},
author = { P. Sarigiannidis and A. Gkaliouris and V. Kakali and M. Louta and G. Papadimitriou and P. Nicopolitidis and M. Obaidat},
url = {https://www.researchgate.net/publication/282688355_On_forecasting_the_ONU_sleep_period_in_XG-PON_systems_using_exponential_smoothing_techniques},
doi = {10.1109/GLOCOM.2014.7037196},
year = {2014},
date = {2014-01-01},
journal = {2014 IEEE Global Communications Conference, GLOBECOM 2014},
pages = {2580-2585},
abstract = {Power management has been advanced on a crucial factor in the design of modern access networks. Furthermore, the proliferation of optical networking in the last mile led major Telecom unions, such as the International Telecommunication Union (ITU), to emerge energy consumption as a critical objective of the next generation passive optical networks (NG-PONs). In particular, the standardization of the 10-gigabit-capable PON (XG-PON) entails well-defined specifications towards power management and energy reduction, especially regarding the power control of optical terminal devices such as the optical network units (ONUs). In this way, the optical line terminator (OLT) along with ONUs are able to cooperate with each other in order to succeed energy reduction, by applying doze or cyclic sleep periods to idle ONUs. However, the sleep period determination remains a quite challenging research area. In this study, we endeavor to provide XG-PON networks with an effective forecasting mechanism that is capable of estimating the time duration of the forthcoming sleep session. To this end, we apply the exponential smoothing technique to best estimate the sleep duration based on the monitoring time series observations. The obtained evaluation results sound quite promising, since the proposed model accomplishes to advance the trade-off between the energy reduction and network efficiency. © 2014 IEEE.},
keywords = {bandwidth allocation, energy efficiency, exponential smoothing, Passive optical networks, XG-PON},
pubstate = {published},
tppubtype = {conference}
}
Power management has been advanced on a crucial factor in the design of modern access networks. Furthermore, the proliferation of optical networking in the last mile led major Telecom unions, such as the International Telecommunication Union (ITU), to emerge energy consumption as a critical objective of the next generation passive optical networks (NG-PONs). In particular, the standardization of the 10-gigabit-capable PON (XG-PON) entails well-defined specifications towards power management and energy reduction, especially regarding the power control of optical terminal devices such as the optical network units (ONUs). In this way, the optical line terminator (OLT) along with ONUs are able to cooperate with each other in order to succeed energy reduction, by applying doze or cyclic sleep periods to idle ONUs. However, the sleep period determination remains a quite challenging research area. In this study, we endeavor to provide XG-PON networks with an effective forecasting mechanism that is capable of estimating the time duration of the forthcoming sleep session. To this end, we apply the exponential smoothing technique to best estimate the sleep duration based on the monitoring time series observations. The obtained evaluation results sound quite promising, since the proposed model accomplishes to advance the trade-off between the energy reduction and network efficiency. © 2014 IEEE.
P. Sarigiannidis; K. Anastasiou; E. Karapistoli; V. Kakali; M. Louta; P. Angelidis
An adaptive power management scheme for Ethernet Passive Optical Networks Conference
2014.
Abstract | BibTeX | Tags: bandwidth allocation, energy efficiency, Learning automata, Passive optical networks | Links:
@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}
}
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.
Address
Internet of Things and Applications Lab
Department of Electrical and Computer Engineering
University of Western Macedonia Campus
ZEP Area, Kozani 50100
Greece
Contact Information
tel: +30 2461 056527
Email: ithaca@uowm.gr
