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001-es BibID:	BIBFORM117943
Első szerző:	Kovásznai Gergely
Cím:	Integer Programming Based Optimization of Power Consumption for Data Center Networks / Gergely Kovásznai, Mohammed Nsaif
Dátum:	2024
ISSN:	0324-721X
Megjegyzések:	With the quickly developing data centers in smart cities, reducing energy consumption and improving network performance, as well as economic benefits, are essential research topics. In particular, Data Center Networks do not always run at full capacity, which leads to significant energy consumption. This paper experiments with a range of optimization tools to find the optimal solutions for the Integer Linear Programming (ILP) model of network power consumption. The study reports on experiments under three communication patterns (near, long, and random), measuring runtime and memory consumption in order to evaluate the performance of different ILP solvers. While the results show that, for near traffic pattern, most of the tools rapidly converge to the optimal solution, CP-SAT provides the most stable performance and outperforms the other solvers for the long traffic pattern. On the other hand, for random traffic pattern, Gurobi can be considered to be the best choice, since it is able to solve all the benchmark instances under the time limit and finds solutions faster by 1 or 2 orders of magnitude than the other solvers do.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény hazai lapban folyóiratcikk integer programming optimization power consumption Data Center Network solvers
Megjelenés:	Acta Cybernetica. - Epub : - (2024), p. 1-17. -
További szerzők:	Nsaif, Mohammed (informatics)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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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
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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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
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM099878
035-os BibID:	(cikkazonosító)3027 (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
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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