Congestion Control Mechanism in Software Defined Networking by Traffic Rerouting

Srikanth, A.; Varalakshmi, P.; Somasundaram, V.; Ravichandiran, Pavithran

doi:10.1109/iccmc.2018.8488041

Cited by 7 publications

(2 citation statements)

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“…TE is a valuable method concerned with study of how to quantity and analyze network traffic in real-time, and develop realistic routing apparatus to schedule and forward traffic flows, to enhance the utilization of the resources of [31]. The goal in TE is to balance traffic flows and optimize routing for traffic delivery, to make the most benefit with network resources.…”

Section: A Te In Traditional Networkmentioning

confidence: 99%

“…In communication networks, load balancing Load balancing represents a major traffic engineering (TE) method whose purpose is to improve the delivery of data traffic loads over various resources driven by a particular performance measure [43] [47]. With regard to hybrid SDN/OSPF, the TE goal of attaining maximum link utilization (MLU) can be made achievable using data traffic flow load balancing to ensure a congestion free network.…”

Section: A Load Balancing Studiesmentioning

confidence: 99%

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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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Section: A Te In Traditional Networkmentioning

confidence: 99%

Section: A Load Balancing Studiesmentioning

confidence: 99%