A greedy power-aware routing algorithm for software-defined networks

Awad, Mohamad Khattar; Rafique, Yousef; Alhadlaq, Sarah Hamad; Hassoun, Dunya; Alabdulhadi, Asmaa; Thani, Sheikha

doi:10.1109/isspit.2016.7886047

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Authors in [21] aims to minimise active links and adapt discrete link rates to traffic load for saving energy. Firstly, they provide a mixed-integer programing definition of the problem.…”

Section: Related Workmentioning

confidence: 99%

Energy aware adaptive resource management model for software‐defined networking‐based service provider networks

Çelenlioğlu

Mantar

2021

IET Networks

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Service provider networks (SPNs) seek to implement the resource management mechanisms called load balancing and energy saving to satisfy today's networking demands. Software-defined networking (SDN) allows SPNs to perform agile and efficient networking operations. It is still an open issue on how to implement these resource management mechanisms in SDN-based SPNs. An energy-aware adaptive resource management model (ERM) for SDN-based SPNs is proposed. Load balancing aims to distribute overall network traffic volume as fairly as possible among available links. Energy-saving aims to turn off as many network devices and links as possible. Unlike the works in the literature, our model utilises the trade-off between load balancing and energy efficiency. To do so, the controller establishes paths among edge pairs. The controller aggregates traffic load between edge pairs to as few links as possible based on a predefined load level. Then, unused network elements switch to sleep mode. In the case of load balancing, the controller distributes traffic load among active network resources. Experimental results show that ERM can successfully increase both energy-saving and resource utilization even under heavy traffic loads. 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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“…Authors in [21] aims to minimise active links and adapt discrete link rates to traffic load for saving energy. Firstly, they provide a mixed-integer programing definition of the problem.…”

Section: Related Workmentioning

confidence: 99%

Energy aware adaptive resource management model for software‐defined networking‐based service provider networks

Çelenlioğlu

Mantar

2021

IET Networks

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“…On the other hand [16,17,18] all talked about implementing ALR in order to reduce energy consumption. Other works like [12,13,19,20,21] all have considered energy aware routing. Authors in reference [20] provided a greedy algorithm-based heuristic and along with links they have also focused on reducing the energy consumption by turning off nodes.…”

Section: Motivationmentioning

confidence: 99%

Energy, Carbon and Renewable Energy: Candidate Metrics for Green-Aware Routing?

Hossain

Georges

Rondeau

et al. 2019

Sensors

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There are all sort of indications that Internet usage will go only upwards, resulting in an increase in energy consumption and CO2 emissions. At the same time, a significant amount of this carbon footprint corresponds to the information and communication technologies (ICT) sector, with around one third being due to networking. In this paper we have approached the problem of green networking from the point of view of sustainability. Here, alongside energy-aware routing, we have also introduced pollution-aware routing with environmental metrics like carbon emission factor and non-renewable energy usage percentage. We have proposed an algorithm based on these three candidate-metrics. Our algorithm provides optimum data and control planes for three different metrics which regulate the usage of different routers and adapt the bandwidth of the links while giving the traffic demand requirements utmost priority. We have made a comparison between these three metrics in order to show their impact on greening routing. The results show that for a particular scenario, our pollution-aware routing algorithm can reduce 36% and 20% of CO2 emissions compared to shortest path first and energy-based solutions, respectively.

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“…Some recent works [17,18,19,20] consider the problem of optimizing the power consumption in SDN using an energy-aware tra c engineering approach that minimizes the number of links. They present formulation of the optimization problem involving routing requirements for control and data plane communications.…”

Section: Energy-aware Routing With Software Defined Networkmentioning

confidence: 99%

Energy-Aware Routing in Software-Defined Network using Compression

Giroire

Huin

Mouliérac

et al. 2018

The Computer Journal

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Software-defined Networks (SDN) is a new networking paradigm enabling innovation through network programmability. Over past few years, many applications have been built using SDN such as server load balancing, virtualmachine migration, tra c engineering and access control. In this paper, we focus on using SDN for energy-aware routing (EAR). Since tra c load has a small influence on the power consumption of routers, EAR allows putting unused links into sleep mode to save energy. SDN can collect tra c matrix and then computes routing solutions satisfying QoS while being minimal in energy consumption. However, prior works on EAR have assumed that the SDN forwarding table switch can hold an infinite number of rules. In practice, this assumption does not hold since such flow tables are implemented in Ternary Content Addressable Memory (TCAM) which is expensive and power-hungry. We consider the use of wildcard rules to compress the forwarding tables. In this paper, we propose optimization methods to minimize energy consumption for a backbone network while respecting capacity constraints on links and rule space constraints on routers. In details, we present two exact formulations using Integer Linear Program (ILP) and introduce e cient heuristic algorithms. Based on simulations on realistic network topologies, we show that using this smart rule space allocation, it is possible to save almost as much power consumption as the classical EAR approach.

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A greedy power-aware routing algorithm for software-defined networks

Cited by 7 publications

References 12 publications

Energy aware adaptive resource management model for software‐defined networking‐based service provider networks

Energy aware adaptive resource management model for software‐defined networking‐based service provider networks

Energy, Carbon and Renewable Energy: Candidate Metrics for Green-Aware Routing?

Energy-Aware Routing in Software-Defined Network using Compression

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