Routing Optimization in SDN using Scalable Load Prediction

Majdoub, Manel; Kamel, Ali El; Youssef, Habib

doi:10.1109/giis48668.2019.9044960

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…The tool often used for achieving this is reinforcement learning (RL) [15], which has also been applied to routing in intercontinental overlay networks [42], showing significant performance improvements over standard IP networks. As indicated earlier, another useful application of RL concerns packet routing in SDN networks [18,43].…”

Section: Optimization Techniques For Sdn Network and The Fogmentioning

confidence: 99%

Smart SDN Management of Fog Services to Optimize QoS and Energy

Fröhlich

Gelenbe

Fiołka

et al. 2021

Sensors

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The short latency required by IoT devices that need to access specific services have led to the development of Fog architectures that can serve as a useful intermediary between IoT systems and the Cloud. However, the massive numbers of IoT devices that are being deployed raise concerns about the power consumption of such systems as the number of IoT devices and Fog servers increase. Thus, in this paper, we describe a software-defined network (SDN)-based control scheme for client–server interaction that constantly measures ongoing client–server response times and estimates network power consumption, in order to select connection paths that minimize a composite goal function, including both QoS and power consumption. The approach using reinforcement learning with neural networks has been implemented in a test-bed and is detailed in this paper. Experiments are presented that show the effectiveness of our proposed system in the presence of a time-varying workload of client-to-service requests, resulting in a reduction of power consumption of approximately 15% for an average response time increase of under 2%.

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