Energy-Aware Routing in Carrier-Grade Ethernet Using SDN Approach

Mâaloul, Rihab; Taktak, Raouia; Fourati, Lamia Chaari; Cousin, Bernard

doi:10.1109/tgcn.2018.2832658

Cited by 41 publications

(18 citation statements)

References 38 publications

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“…To see the effectiveness of the PS, we compare it to two other approaches named minimum link on (MLO) and MNO [6 ]. In MLO, routing of flows is performed such that the number of active links in the network is minimised.…”

Section: Simulation Resultsmentioning

confidence: 99%

Impact of TCAM size on power efficiency in a network of OpenFlow switches

Ghiasian

2020

IET Networks

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Recent developments in networking include the interest in software-defined networking, in which OpenFlow switches are in charge of forwarding traffic by matching incoming packets with forwarding rules in their flow tables. Flow tables are commonly implemented in ternary content addressable memories (TCAMs). Typically, this type of memory tends to be extremely expensive and to be power-hungry devices. The price and power dissipation of TCAM depend on the capacity of the memory. A blind design of memory size can result in dramatic degradation in performance and power efficiency of the entire network. In this study, the authors clarify this impact and show how the optimum value of TCAM capacity is attainable. They also propose a power consumption model for TCAM-based OpenFlow switches. Simulation results show that the proposed solution reduces the average power consumption by 24% compared to the minimum link on method and by 11.5% compared to the minimum node on (MNO) method and thus power efficiency is better achieved when each memory is supplied by appropriate TCAM size.

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Section: Simulation Resultsmentioning

confidence: 99%

Impact of TCAM size on power efficiency in a network of OpenFlow switches

Ghiasian

2020

IET Networks

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“…Our approach belongs to the Traffic Aware Energy efficiency approaches presented in this paper. The first-fit heuristic approach was used in [31]. Nodes and links are sorted using three criteria: Most-Power (MP), Least-Flow (LF), and Random (R).…”

Section: Network Energy Aware Approaches and Methodsmentioning

confidence: 99%

On adaptive multi-objective optimization for greener wired networks

Yazbek

Liu

2020

SN Appl. Sci.

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“…This condition can be a significant limitation to energyefficient routing in practical networks. It is observed that only SDN nodes and the connected links can get into sleep Finally, Maaloul et al, [67] considered energy saving problem in the case of carrier-grade Ethernet network. In their work, the authors suggested a traffic-aware routing and load balancing scheme to shut down the minimum set of network elements and links, so as to achieve energy-saving and QoS guarantees free of network performance degrading.…”

Section: A Load Balancing Studiesmentioning

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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Energy-Aware Routing in Carrier-Grade Ethernet Using SDN Approach

Cited by 41 publications

References 38 publications

Impact of TCAM size on power efficiency in a network of OpenFlow switches

Impact of TCAM size on power efficiency in a network of OpenFlow switches

On adaptive multi-objective optimization for greener wired networks

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

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