Estimating SDN Traffic Matrix Based on Online Informative Flow Measurement Method

Li, Dongyang; Dai, Ningyun; Feng, Li; Xing, Changyou; Dai, Fei

doi:10.1109/cbd.2017.21

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…The logic of forwarding packets is set by the controller for implementation in the OpenFlow flow table, which is the key to traffic matrix calculation. Therefore, we need a statistical method to obtain traffic matrices, which can make SDNs more scalable, as follows [150]:…”

Section: Traffic Matrix Modelingmentioning

confidence: 99%

A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges

Islam

Al-Mukhtar

Khan

et al. 2023

Eng

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The Software-Defined Network (SDN) is a next-generation network that uses OpenFlow to decouple the control plane from the data plane of forwarding devices. Other protocols for southbound interfaces include ForCES and POF. However, some security issues might be in action on the SDN, so that attackers can take control of the SDN control plane. Since live video calling, QoS control, high bandwidth needs, and resource management are inevitable in any SDN/Software-Defined Cellular Network (SDCN), traffic monitoring is an integral approach for safeguarding against DDoS, heavy hitters, and superspreaders. In such a scenario, SDN traffic measurement comes into action. Thus, we survey SDN traffic measurement solutions to assess how these solutions can make a secure, efficient, and robust SDN/SDCN architecture. This research classifies SDN traffic measurement solutions according to network application behavior and compares several ML approaches. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and the future scope of research, which will guide the design and development of more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical, and widely deployed SDN/SDCN architecture. In more detail, we list different kinds of practical machine learning (ML) approaches to analyze how we can improve traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will help secure SDNs/SDCNs in complementary ways.

show abstract

Section: Traffic Matrix Modelingmentioning

confidence: 99%

A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges

Islam

Al-Mukhtar

Khan

et al. 2023

Eng

View full text Add to dashboard Cite

show abstract

“…The logic of forwarding packets is set by the controller for implementation in the OpenFlow flow table, which is the key to traffic matrix calculation. Therefore, we need a statistical method to get traffic matrices which can make SDN more scalable [144].…”

Section: Traffic Matrix Modellingmentioning

confidence: 99%

A Survey on SDN \& SDCN Traffic Measurement: Existing Approaches and Research Challenge

Islam¹,

Hossain²,

AlMukhtar³

2022

Preprint

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Software Defined Network (SDN) is the next generation network that decouples the control plane from the data plane of forwarding devices by utilizing the OpenFlow protocol as a communication link between the data plane and the control plane. However, there are some security issues might be in actions on SDN that the attackers can take control over the SDN control plane. Thus, traffic measurement is a fundamental technique of protecting SDN against the high-security threats such as DDoS, heavy hitter, superspreader as well as live video calling, QoS control, high bandwidth requirement, resource management are also inevitable in SDN/Software Defined Cellular Network (SDCN). In such a scenario, we survey SDN traffic measurement solutions, in order to assess how these solutions can make a secured, efficient and robust SDN/SDCN architecture. In this paper, various types of SDN traffic measurement solutions have been categorized based on network applications behaviour. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and future scope of research, which will guide to design and develop more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical and widely deployed SDN/SDCN in future prospects. More in details, we list out kinds of practical machine learning (ML) approaches to analyze how we can make improvement in the traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will give benefit to get secured SDN/SDCN network in complementary ways.

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“…Besides, Tian Yang in [23] presented a new framework for solving the problem of network traffic measurement in SDN-based IP networks. Based on the low rank and time-varying characteristics of traffic flows, the authors in [24] proposed a novel mechanism for the on-line network traffic measurement by utilizing the flow measurement ability of the SDN technique. In [25], an SDN-based fine-grained measurement method in vehicular communication network was proposed.…”

Section: Related Workmentioning

confidence: 99%

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale ITS-Oriented Backbone Networks

et al. 2020

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The end-to-end network traffic information is the basis of network management for a large-scale intelligent transportation systems-oriented backbone network. To obtain exact network traffic data, a prevalent idea is to deploy NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, but also affects the network load. Motivated by this issue, we propose an optimized traffic measurement method based on reinforcement learning in this paper, which can collect most of the network traffic data by activating NetFlow on a subset of interfaces of routers in a network. We use the Q-learning-based approach to deal with the problem of the interface-selection. We propose an approach to compute the reward, furthermore a modified Q-learning approach is proposed to handle the problem of interface-selection. The method is evaluated by the real data from the Abilene and GÉANT backbone networks. Simulation results show that the proposed method can improve the efficiency of traffic measurement distinctly. INDEX TERMS Network measurement, reinforcement learning, intelligent transportation systems, IP backbone network.

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Estimating SDN Traffic Matrix Based on Online Informative Flow Measurement Method

Cited by 11 publications

References 7 publications

A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges

A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges

A Survey on SDN \& SDCN Traffic Measurement: Existing Approaches and Research Challenge

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale ITS-Oriented Backbone Networks

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