Traffic engineering in software-defined networking: Measurement and management

Shu, Zhaogang; Wan, Jiafu; Lin, Jintong; Wang, Shiyong; Li, Di; Rho, Seungmin; Yang, Changcai

doi:10.1109/access.2016.2582748

Cited by 168 publications

(65 citation statements)

References 52 publications

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“…Although there are numerous available mechanisms for traffic analysis, traffic flow management and resilience [2] in SDN based networks, some significant challenges still remain to be addressed [3]. In particular, adapting the granularity of traffic forwarding while protecting forwarding devices from an overflow situation is one critical issue.…”

Section: Introductionmentioning

confidence: 99%

Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning

Islam

Nguyen

Bauschert

2019

2019 15th International Conference on Network and Service Management (CNSM)

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Software-Defined Networking (SDN) introduces a centralized network control and management by separating the data plane from the control plane which facilitates traffic flow monitoring, security analysis and policy formulation. However, it is challenging to choose a proper degree of traffic flow handling granularity while proactively protecting forwarding devices from getting overloaded. In this paper, we propose a novel traffic flow matching control framework called Q-DATA that applies reinforcement learning in order to enhance the traffic flow monitoring performance in SDN based networks and prevent traffic forwarding performance degradation. We first describe and analyse an SDN-based traffic flow matching control system that applies a reinforcement learning approach based on Q-learning algorithm in order to maximize the traffic flow granularity. It also considers the forwarding performance status of the SDN switches derived from a Support Vector Machine based algorithm. Next, we outline the Q-DATA framework that incorporates the optimal traffic flow matching policy derived from the traffic flow matching control system to efficiently provide the most detailed traffic flow information that other mechanisms require. Our novel approach is realized as a REST SDN application and evaluated in an SDN environment. Through comprehensive experiments, the results show that-compared to the default behavior of common SDN controllers and to our previous DATA mechanism-the new Q-DATA framework yields a remarkable improvement in terms of traffic forwarding performance degradation protection of SDN switches while still providing the most detailed traffic flow information on demand.

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Section: Introductionmentioning

confidence: 99%

Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning

Islam

Nguyen

Bauschert

2019

2019 15th International Conference on Network and Service Management (CNSM)

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“…Moreover, the control plane is programmable to support the flexible deployment of new network functions. The distinguishing features enable SDN to provide support for traffic engineering to improve network performance and simplify network management . More importantly, these good features of SDN can greatly facilitate massive data acquisition and processing .…”

Section: Introductionmentioning

confidence: 99%

“…The distinguishing features enable SDN to provide support for traffic engineering to improve network performance and simplify network management. 20 More importantly, these good features of SDN can greatly facilitate massive data acquisition and processing. 21 By leveraging the logical centralized control and the powerful computational capability, the SDN controller makes it easier to collect and process massive network traffic.…”

Section: Introductionmentioning

confidence: 99%

Deep learning–based network application classification for SDN

Zhang

Wang

et al. 2018

Trans Emerging Tel Tech

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Accurate application classification is important and useful to improve network performance. However, with the continuous expansion of network scale and the rapid increase of network users, it is very difficult for the existing application classification methods to accurately identify and classify network applications. Currently, most classification methods are suitable for small‐scale data sets and cannot achieve high classification accuracy because of the shallow learning structure and the limited learning ability. The emergence of deep learning technology and software‐defined networking (SDN) enables the application classification method to process large‐scale data. In this paper, by leveraging the SDN architecture, we present a novel hybrid deep neural network–based application classification method, which achieves high classification accuracy without the manual feature selection and extraction. In the proposed application classification framework, by taking the advantage of the logical centralized control and powerful computing capability, the massive network traffic is easily collected and processed by the SDN controller. The processed data is used to train the hybrid deep neural network, which is composed of the stacked autoencoder and softmax regression layer. The deep flow features can be obtained from the stacked autoencoder automatically instead of the manual feature selection and extraction. The softmax regression layer is used as the classifier to realize the application classification. Finally, simulation results demonstrate that our proposed classification method is effective and gets higher classification accuracy than the support vector machine–based classification method.

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“…Fig.2. LLDP for SDN topology [7] As mention before, the first action from controller is sending out the packet as Packet-out message with LLDP packet to the first switch. At the beginning, the second switches will first broadcast its LLDP packet as Packet-out message to find another switch neighbours.…”

Section: Congestion Control In Sdnmentioning

confidence: 99%

“…2. According to the work in [7], LLDP has been proposed where SDN controller sends packets as Packet-out message during process link discovery. The packets will be sent to all switches in the network and when SDN switch receive LLDP packet, it sends the packet to all other switches directly connected to it.…”

Section: Congestion Control In Sdnmentioning

confidence: 99%

Performance Analysis of Congestion Control Mechanism in Software Defined Network (SDN)

et al. 2017

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Abstract. In the near future, the traditional networks architecture will be difficult to be managed. Hence, Software Defined Network (SDN) will be an alternative in the future of programmable networks to replace the conventional network architecture. The main idea of SDN architecture is to separate the forwarding plane and control plane of network system, where network operators can program packet forwarding behaviour to improve the network performance. Congestion control is important mechanism for network traffic to improve network capability and achieve high end Quality of Service (QoS). In this paper, extensive simulation is conducted to analyse the performance of SDN by implementing Link Layer Discovery Protocol (LLDP) under congested network. The simulation was conducted on Mininet by creating four different fanout and the result was analysed based on differences of matrix performance. As a result, the packet loss and throughput reduction were observed when number of fanout in the topology was increased. By using LLDP protocol, huge reduction in packet loss rate has been achieved while maximizing percentage packet delivery ratio.

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Traffic engineering in software-defined networking: Measurement and management

Cited by 168 publications

References 52 publications

Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning

Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning

Deep learning–based network application classification for SDN

Performance Analysis of Congestion Control Mechanism in Software Defined Network (SDN)

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