Framework for Traffic Proportional Energy Efficiency in Software Defined Networks

Assefa, Beakal Gizachew; Özkasap, Öznur

doi:10.1109/blackseacom.2018.8433618

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…Meanwhile, the study proposes two heuristic algorithms for the same purpose and reports on experiments executed in two scenarios: synthetic topology and real-life topology named CERNET. In the same context, another study in [2] proposes an ILP model with a multi-objective function to minimize the sum of the energy consumption of switches and links. The study limits the links' maximum and minimum utility to manage the trade-off between the power consumption and the network performance.…”

Section: Related Workmentioning

confidence: 99%

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Integer Programming Based Optimization of Power Consumption for Data Center Networks

Kovásznai,

Nsaif

2023

Acta Cybern

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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.

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Section: Related Workmentioning

confidence: 99%

“…The model in [14] employed only the second operand from (1), to minimize the number of the links as shown in (2). min…”

Section: Ilp Formulationmentioning

confidence: 99%

Integer Programming Based Optimization of Power Consumption for Data Center Networks

Kovásznai,

Nsaif

2023

Acta Cybern

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“…Assefa and Ozkasap [139] proposed a hybrid SDN-enabled energy-efficient routing framework that employs ML and RL technique to improve link performance. The researchers considered a strategy where network traffic is represented as features, upon which feature size reduction can be performed using numerically proven algorithmic techniques, to provide…”

Section: ML and Dl Techniques For Energy-efficient Routing In Sdnmentioning

confidence: 99%

“…Testing means the application of the trained prediction model to predict the unknown future traffic demands. We use cross-validation technique to select the right parameter k in an n-fold cross validation [139]. To perform model testing, the RNN-LSTM network traffic prediction model is checked based on a subset of features.…”

Section: Rnn-lstm Network Traffic Prediction Model Testing and Validationmentioning

confidence: 99%

AI-Assisted Framework for Green-Routing and Load Balancing in Hybrid Software-Defined Networking: Proposal, Challenges and Future Perspective

et al. 2020

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The explosive growth of IP networks, the advent of cloud computing, and the rapid progress in wireless communications witnessed today reflect significant progress towards meeting the explosive data traffic demands. Consequently, communications service providers should deploy efficient and intelligent network solutions to accommodate the huge traffic demands and to ease the capacity pressure on their network infrastructure. Besides, vendors should develop novel energy-efficient networks to reduce network utility costs and carbon footprint. Software-defined networking (SDN) provides a suitable solution, however, complete SDN deployment is currently unachievable in the short-term. An alternative is the hybrid SDN/ open shortest path forwarding (OSPF) network, which allows the deployment of SDN in legacy networks. Nevertheless, hybrid SDN/OSPF also faces several technical, economic and organizational challenges. Although many energyefficiency routing solutions exist in hybrid SDN/OSPF networks, they are generic and reactive by design. Moreover, these solutions are characterized by manual control plane forwarding configurations, leading to sub-optimal performance. The recent promising combination of SDN and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) in traffic management and control provides tremendous opportunities. In this paper, we first provide a review of the most recent optimization approaches for global energy-efficient routing and load balancing. Next, we investigate a scalable and intelligent integrated architectural framework that leverages deep reinforcement learning (DRL) techniques to realize predictive and rate adaptive energy-efficient routing with guaranteed quality of service (QoS), in transitional hybrid SDN/OSPF networks. Based on the need to minimize global network energy consumption and improve link performance, this paper provides key research insights into the current progress in hybrid SDN/OSPF, ML and AI in the hope of stimulating more research.

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“…Several studies have been performed for the energy aware routing scheme to minimize the energy consumption and carbon footprint reduction to show that IPv6 and SDN have greater flexibility towards green networking (Chabarek et al, ; Gaddour, Koubâa, & Abid, ; Gesi, ; Giroire, Moulierac, & Phan, ; Hu, Luo, Wang, & Deng, ; Maaloul et al, ; Ruiz‐Rivera, Chin, & Soh, ; Saad, Chauvenet, & Tourancheau, ; Tuysuz, Ankarali, & Gözüpek, ; Zhu, Liao, De Laat, & Grosso, ). SDN devices being programmable and controllable, network switches and links can enter into low power state in the sleep mode and the device architecture can be designed to have actual load based proportional energy consumption (Assefa & Ozkasap, ). Hence, energy saving in SDN is viable algorithmically or through hardware improvements (Chiaraviglio, Ciullo, Mellia, & Meo, ; Coiro, Chiaraviglio, Cianfrani, Listanti, & Polverini, ; Kun, Xiaohong, Maode, & Pei, ; Moaiyeri, Doostaregan, & Navi, ; Rawat & Reddy, ; Salehi & DeMara, ).…”

Section: Software Defined Ipv6 Network and The Green Networkingmentioning

confidence: 99%

Towards energy efficiency and green network infrastructure deployment in Nepal using software defined IPv6 network paradigm

Dawadi

Rawat

Joshi

et al. 2019

E J Info Sys Dev Countries

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The use of information and communication technology (ICT) has resulted in significant impacts on social welfare, economic growth, transparency, and good governance in developing countries like Nepal. Due to the diverse geographic and economic situations, ICT network and service expansions throughout Nepal have been becoming quite challenging. Private network operators mostly have confined their services to urban areas. Nepal Telecommunications Authority (NTA) collects 2% royalty form Internet Service Providers (ISPs) and Telecom Operators as Rural Telecommunications Disbursement Fund (RTDF) to enhance ICT services to rural Nepal. Broadband expansion projects initiated by utilizing RTDF to expand ICT infrastructure throughout Nepal are expected to have considerable societal and economical transformations in the rural communities of Nepal. This paper not only presents the current ICT deployment scenario of Nepal but also studies design, analysis, and evaluation of green networking that leverages both software defined networking (SDN), and Internet Protocol version 6 (IPv6)-aka Software Defined IPv6 (SoDIP6)-for energy efficient networking, robust services, and sustainable ICT ecosystem for developing nations like Nepal. We evaluate the SoDIP6 network by considering a typical ISP with end access networks and present benefits and recommendations. Experimental results show that the proposed SoDIP6 network help significantly reduce the energy consumption and carbon footprint leading to overall economic benefits to service providers and the society. Furthermore, energy-saving practices through SoDIP6 networks and some policy directions to the government to focus on green networking considering sensitivity of climate change and global warming impact in the mountainous and developing countries like Nepal are presented.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Framework for Traffic Proportional Energy Efficiency in Software Defined Networks

Cited by 10 publications

References 18 publications

Integer Programming Based Optimization of Power Consumption for Data Center Networks

Integer Programming Based Optimization of Power Consumption for Data Center Networks

AI-Assisted Framework for Green-Routing and Load Balancing in Hybrid Software-Defined Networking: Proposal, Challenges and Future Perspective

Towards energy efficiency and green network infrastructure deployment in Nepal using software defined IPv6 network paradigm

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