Load-balancing routing in software defined networks with multiple controllers

Wang, Haibo; Xu, Hongli; Huang, Liusheng; Wang, Jianxin; Yang, Xuwei

doi:10.1016/j.comnet.2018.05.012

Cited by 46 publications

(15 citation statements)

References 9 publications

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“…When failure occurs, the response time of the controller increases and it leads to poor network management. Another similar research can be found in [27] and it is called load-balanced aware routing on the SDN controller. It addresses the problem of load balance routing in both controllers and links and thus it minimizes the controller response time and link utilization rate.…”

Section: Routing Schemesmentioning

confidence: 96%

AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT

Begovic¹,

Čaušević²,

Memić³

et al. 2020

International Journal of Engineering Research and Technology

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Internet of Things (IoT) becomes an emerging network technology that expedites billions of devices to be connected via the Internet to provide real-time intelligent application services. The benefits of Software-Defined Networking (SDN) can be used to fulfill IoT requirements. Quality of Service provisioning is an ongoing demand in software-defined IoT (SD-IoT), particularly for large scale environments. In this paper, we address this issue by proposing a seamless model of AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in distributed fragmentation optimized SDN-IoT. Firstly, we propose a Multi-Criterion based Deep Packet Inspection method for classifying the network traffic, which is held in Edge Routers (access points). Secondly, we construct a Partially Connected Network Topology using the ISOMAP algorithm for an effective rule placement and routing. We propose a Traffic Differentiated QoS Routing for forwarding data packets via the most suitable switches. We select the optimum route by Deep Alternative Neural Network (DANN). Based on the relationships among switches, the path is selected and flow rules are deployed. The poor QoS is often caused by load imbalance in controllers and switches. To overwhelm this issue, we propose a Dynamic Offloading scheme in SD-IoT. We offload the data packets from the overloaded controller to the underloaded controller using Hassanat Distance-based Knearest neighbors (HDK-NN) algorithm. Similarly, we propose a Ranking-based Entropy function (R-Ef) to allow dynamic offloading among switches. Simulation is performed using the NS3.26 simulator and the results proved that our proposed AIaided SD-IoT model provides superior QoS performance compared to previous approaches.

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Section: Routing Schemesmentioning

confidence: 96%

AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT

Begovic¹,

Čaušević²,

Memić³

et al. 2020

International Journal of Engineering Research and Technology

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“…The authors in [106] explicitly describe the load balance and low delay route deployment problems for the control connection and illustrate NP-hardness to improve QoS by taking the control link constraints and other data plane restrictions in SDN into consideration. The issue of performing controller load balancing and link load balancing is formulated as NP-Hard in [118]. The rounding-based algorithm is proposed to solve the problem and provide better scalability and reduce the load.…”

Section: B Artificial Intelligence Based 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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“…Wang et al [29] proposed a rounding-based load balancing solution to reduce control channel congestion and response time for SDNs having multiple controllers. This algorithm performs link load balancing and controller load balancing.…”

Section: Related Workmentioning

confidence: 99%

Reducing the effects of DoS attacks in software defined networks using parallel flow installation

Imran

Durad

Khan

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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Software defined networking (SDN) is becoming more and more popular among the network research community due to its simple configuration and easy management. SDN makes it easy to get fine-grained information about the transferred data and provides centralized control over network traffic. Therefore, it can manage all network traffic with different protocols, such as Internet Protocol version 6 (IPv6), Internet Protocol version 4 (IPv4), Internet Control Message Protocol (ICMP), Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), etc., from different sources (MAC address, IP address, port number, etc.) in multiple ways [1, 2]. In addition, SDNs are more scalable and flexible as compared to legacy networks. In SDN,

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Load-balancing routing in software defined networks with multiple controllers

Cited by 46 publications

References 9 publications

AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT

AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT

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

Reducing the effects of DoS attacks in software defined networks using parallel flow installation

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