A load balanced congestion aware routing mechanism for Software Defined Networks

Attarha, Shadi; Hosseiny, Koosha Haji; Mirjalily, Ghasem; Mizanian, Kiarash

doi:10.1109/iraniancee.2017.7985428

Cited by 22 publications

(10 citation statements)

References 2 publications

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“…Third, congestion-aware routing for an SDN is proposed. Attarha et al [30] proposed a method to reroute a flow to avoid congestion in an SDN. To make a decision, link utilization is periodically measured and reported.…”

Section: Congestion-aware Routingmentioning

confidence: 99%

Q-Learning Based Routing Protocol for Congestion Avoidance

Godfrey¹,

Kim²,

Miao³

et al. 2021

Computers, Materials &Amp; Continua

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The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol, either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state, Q-value, and reward function to set the desired path toward the destination. A new reward function that consists of a buffer occupancy, link reliability and hop count is considered. Moreover, look ahead algorithm is employed to update the Q-value with values within two hops simultaneously. This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account, accordingly. Finally, the simulation results presented approximately 20% higher packet delivery ratio and 15% shorter end-to-end delay, compared to those with the existing scheme by avoiding congestion adaptively.

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Section: Congestion-aware Routingmentioning

confidence: 99%

Q-Learning Based Routing Protocol for Congestion Avoidance

Godfrey¹,

Kim²,

Miao³

et al. 2021

Computers, Materials &Amp; Continua

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“…Network congestion is a result of increasing load on the network and degrades the QoS. So re-routing the traffic is a solution, where the authors in [56], [62], [70], [72], [93], [95] have used and modified the flow rules to improve the degree of load balancing.…”

Section: A Conventional Load Balancing Techniquesmentioning

confidence: 99%

A Systematic Review of Load Balancing Techniques in Software-Defined Networking

et al. 2020

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The traditional networks are facing difficulties in managing the services offered by cloud computing, big data, and the Internet of Things as the users have become more dependent on their services. Software-Defined Networking (SDN) has pulled enthusiasm in the integration process of technologies and function as per the user's requirements for both academia and industry, and it has begun to be embraced in actual framework usage. The emergence of SDN has given another idea to empower the focal programmability of the system. Because of the increasing demand and the scarcity of resources, the load balancing issue needs to be addressed efficiently to manage the incoming traffic and resources and to improve network performance. One of the most critical issues is the role of the controller in SDN to balance the load for having a better Quality of Service (QoS). Though there are few survey articles written on load balancing, there is no detail and systematic review conducted in load balancing in SDN. Hence, this paper extends and reviews the discussion with a taxonomy of current emerging load balancing techniques in SDN systematically by categorizing the techniques as conventional and artificial intelligence-based techniques to improve the service quality. The review also includes the study of metrics and parameters which have been used to measure the performance. This review would allow gaining more information on load balancing approaches in SDN and enables the researchers to fill the current research gaps.

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“…Each switch responds by sending ofp_port_stats_reply message. Controller then computes port utilisation for each port using transmitted bytes count tx_bytes using the formula: (1) If the port utilization exceeds the fixed threshold T= 70% [21] for any port, links associated with the port is identified as bottleneck link, accordingly the bottleneck links list will be updated in every 10 seconds. Algorithm 1 computes port utilization and identifies bottleneck link as illustrated below.…”

Section: Identifying Bottleneck Linkmentioning

confidence: 99%

A Proactive Flow Admission and Re-Routing Scheme for Load Balancing and Mitigation of Congestion Propagation in SDN Data Plane

Sminesh

Kanaga

Ranjitha

2018

IJCNC

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The centralized architecture in software-defined network (SDN) provides a global view of the underlying network, paving the way for enormous research in the area of SDN traffic engineering (SDN TE). This research focuses on the load balancing aspects of SDN TE, given that the existing reactive methods for data-plane load balancing eventually result in packet loss and proactive schemes for data plane load balancing do not address congestion propagation. In the proposed work, the SDN controller periodically monitors flow level statistics and utilization on each link in the network and over-utilized links that cause network congestion and packet loss are identified as bottleneck links. For load balancing the identified largest flow and further traffic through these bottleneck links are rerouted through the lightly-loaded alternate path. The proposed scheme models a Bayesian Network using the observed port utilization and residual bandwidth to decide whether the newly computed alternate path can handle the new flow load before flow admission which in turn reduces congestion propagation. The simulation results show that when the network traffic increases the proposed method efficiently re-routes the flows and balance the network load which substantially improves the network efficiency and the quality of service (QoS) parameters.

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A load balanced congestion aware routing mechanism for Software Defined Networks

Cited by 22 publications

References 2 publications

Q-Learning Based Routing Protocol for Congestion Avoidance

Q-Learning Based Routing Protocol for Congestion Avoidance

A Systematic Review of Load Balancing Techniques in Software-Defined Networking

A Proactive Flow Admission and Re-Routing Scheme for Load Balancing and Mitigation of Congestion Propagation in SDN Data Plane

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