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001-es BibID:BIBFORM124271
Első szerző:Nsaif, Mohammed (informatics)
Cím:Evaluating RNN Models for Multi-Step Traffic Matrix Prediction / Mohammed Nsaif, Gergely Kovásznai, Hasanein D. Rjeib, Ali Malik, Ruairí de Fréin
Dátum:2024
Megjegyzések:Network traffic matrix prediction is used to estimate the patterns of future network flows before they are initiated. Traffic matrix prediction is needed by a wide range of network management functions such as network monitoring and it relies on historical data. In this paper, we address the task of multi-time step traffic matrix prediction using Recurrent Neural Networks (RNN). Our objective is to conduct a comparative study of different types of RNNs and to evaluate their ability to predict multi-time step Origin-Destination traffic matrices. Experiments show that RNNs are capable of predicting multiple steps of traffic matrices, however, the RMSE of the predictions increases as we move further away from the last true value. Our primary finding is that the RNN-GRU show has the best prediction ability in the very beginning steps with an RMSE of 0.048, while RNN-LSTM demonstrated higher capability with the last steps, having an RMSE value of 0.058.
ISBN:979-8-3503-8788-9
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
Traffic Matrix
Neural Networks
Multi-steps Prediction
GEANT Dataset
Megjelenés:2024 IEEE 3rd Conference on Information Technology and Data Science (CITDS) Proceedings / ed. András Hajdu. - p. 152-157. -
További szerzők:Kovásznai Gergely Rjeib, Hasanein D. Malik, Ali de Fréin, Ruairí
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
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2.

001-es BibID:BIBFORM123232
035-os BibID:(Scopus)85200764414 (WoS)001286885000001
Első szerző:Nsaif, Mohammed (informatics)
Cím:QoS-Aware Power-Optimized Path Selection for Data Center Networks (Q-PoPS) / Mohammed Nsaif, Gergely Kovásznai, Ali Malik, Ruairí de Fréin
Dátum:2024
ISSN:2079-9292
Megjegyzések:Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
data center networks
energy efficiency
quality of service
software-defined networking
real-time traffic
power optimization
QoS-aware path selection
Megjelenés:Electronics (Switzerland). - 13 : 15 (2024), p. 1-26. -
További szerzők:Kovásznai Gergely Malik, Ali de Fréin, Ruairí
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3.

001-es BibID:BIBFORM121724
035-os BibID:(Scopus)85195385631 (WoS)001248139200001
Első szerző:Nsaif, Mohammed (informatics)
Cím:SM-FPLF : Link-State Prediction For Software-Defined DCN Power Optimization / Mohammed Nsaif, Gergely Kovásznai, Ali Malik, Ruairí De Fréin
Dátum:2024
ISSN:2169-3536
Megjegyzések:Efficientmonitoring systems that optimize resource allocation, reduce energy usage through machine learning and flow aggregation routing techniques, are needed due to the escalating power consumption of data center networks, which, as has been recently reported, account for up to eight percent of global energy consumption, posing environmental operational concerns. We propose a software-defined data-center monitoring algorithm that reduces power consumption by (1) using a GPU implementation of a Stacked Long Short-Term Memory Recurrent Neural Network (RNN) model for link utilization prediction, thus reducing monitoring overhead, and (2) utilizing a flow aggregation routing algorithm with feedback from online, OpenFlow-powered monitoring and machine learning modules. This combined approach results in a new algorithm called SMart-Fill Prefer Path First (SM-FPLF). In the context of SM-FPLF, the objective of this paper is to compare the (1) training and validation loss curves for various models, (2) to evaluate the prediction accuracy of learning approaches for a range of prediction horizons, (3) to assess the time-cost and accuracy for different models, with a specific focus on the GuSLSTM and GuGRU models, (4) to analyze OpenFlow traffic with and without using the preferred prediction algorithm, the GuSLSTMmodel, assessing the accumulated power consumption per OpenFlow channel in the data-centre when SM-FPLF is applied. Our findings indicate that the GuSLSTM outperforms rival algorithms in terms of link utilization prediction accuracy over varying input sequence lengths. This accuracy is achieved whilst satisfying the SDN domain-specific requirement of a small computation time in a real-time implementation. Embedding a GuSLSTM in the SM-FPLF algorithm offers a power saving of 372 watts per OpenFlow channel, which is achieved in part due to a 13.7% CPU usage reduction in controllers and switches. These findings provide a valuable perspective into the performance and suitability of RNNs for real-time implementation as part of SDN solutions. They also shed light on their practical implications and benefits of using link utilization prediction in SDN management and power consumption optimization solutions.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Data center networks
software-defined networks
power consumption
machine learning
OpenFlow
monitoring
prediction
overhead
Megjelenés:IEEE Access. - 12 (2024), p. 79496-79518. -
További szerzők:Kovásznai Gergely Malik, Ali de Fréin, Ruairí
Pályázati támogatás:13/RC/2077_P2
Egyéb
15/SIRG/3459
Egyéb
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4.

001-es BibID:BIBFORM117945
035-os BibID:(Scopus)85141002485
Első szerző:Nsaif, Mohammed (informatics)
Cím:ML-Based Online Traffic Classification for SDNs / Mohammed Nsaif, Gergely Kovasznai, Mohammed Abboosh, Ali Malik, Ruairí de Frein
Dátum:2022
Megjegyzések:Traffic classification is a crucial aspect for Software- Defined Networking functionalities. This paper is a part of an on-going project aiming at optimizing power consumption in the environment of software-defined datacenter networks. We have developed a novel routing strategy that can blindly balance between the power consumption and the quality of service for the incoming traffic flows. In this paper, we demonstrate how to classify the network traffic flows so that the quality of service of each flow-class can be guaranteed efficiently. This is achieved by creating a dataset that encompasses different types of network traffic such as video, VoIP, game and ICMP. The performance of a number of Machine Learning techniques is compared and the results are reported. As part of future work, we will incorporate classification into the power consumption model towards achieving further advances in this research area.
ISBN:9781665496537
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
Machine learning
classification
dataset
SDN
Megjelenés:2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS). - p. 217-222. -
További szerzők:Kovásznai Gergely Abboosh, Mohammed (informatics) Malik, Ali de Fréin, Ruairí
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5.

001-es BibID:BIBFORM116499
035-os BibID:(WoS)001143123700007 (Scopus)85174848207
Első szerző:Nsaif, Mohammed (informatics)
Cím:Survey of Routing Techniques-Based Optimization of Energy Consumption in SD-DCN / Mohammed Nsaif, Gergely Kovásznai, Ali Malik, Ruairí de Fréin
Dátum:2023
ISSN:2061-2079
Megjegyzések:The increasing power consumption of Data Center Networks (DCN) is becoming a major concern for network operators. The object of this paper is to provide a survey of state-of-the-art methods for reducing energy consumption via (1) enhanced scheduling and (2) enhanced aggregation of traffic flows using Software-Defined Networks (SDN), focusing on the advantages and disadvantages of these approaches. We tackle a gap in the literature for a review of SDN-based energy saving techniques and discuss the limitations of multi-controller solutions in terms of constraints on their performance. The main finding of this survey paper is that the two classes of SDN- based methods, scheduling and flow aggregation, significantly reduce energy consumption in DCNs. We also suggest that Machine Learning has the potential to further improve these classes of solutions and argue that hybrid ML-based solutions are the next frontier for the field. The perspective gained as a consequence of this analysis is that advanced ML-based solutions and multi-controller-based solutions may address the limitations of the state-of-the-art, and should be further explored for energy optimization in DCNs.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény hazai lapban
folyóiratcikk
Data Center
Energy
Integer Programming
Power Consumption
Routing
Software-Defined Networking
Megjelenés:Infocommunications Journal. - 15 : Special Issue (2023), p. 35-42. -
További szerzők:Kovásznai Gergely Malik, Ali de Fréin, Ruairí
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6.

001-es BibID:BIBFORM099878
035-os BibID:(WoS)000734578500001 (Scopus)85120608831
Első szerző:Nsaif, Mohammed (informatics)
Cím:An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks / Mohammed Nsaif, Gergely Kovásznai, Anett Rácz, Ali Malik, Ruairí de Fréin
Dátum:2021
ISSN:2079-9292
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Electronics (Switzerland). - 10 : 23 (2021), p. 1-18. -
További szerzők:Kovásznai Gergely Rácz Anett (1983-) (programtervező matematikus) Malik, Ali de Fréin, Ruairí
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