On Using Flow Classification to Optimize Traffic Routing in SDN Networks

Yahyaoui, Haythem; Aidi, Saifeddine; Zhani, Mohamed Faten

doi:10.1109/ccnc46108.2020.9045216

Cited by 14 publications

(6 citation statements)

References 11 publications

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“…In [220], a framework called FlowStat uses per-flow statistics to produce knowledge on optimal paths using max-flow min-cost optimization and then uses integer linear programming as the rule generator to decide forwarding rules for the forwarding paths. A routing framework that classifies the data flow into two classes (mice and elephant) based on their size and then, based on the knowledge of the classified flow type, uses a machine learning technique called association rules to generate forwarding rules for each flow class [221]. Ternary Content Addressable Memory (TCAM) modules have a limited capacity and are used to store flow rules in KDNs.…”

Section: Examples Using Composed Knowledge In the Existing Literaturementioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

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Section: Examples Using Composed Knowledge In the Existing Literaturementioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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“…This section presents studies on optimizing network performance by identifying and managing elephant and mice flows. In [23], a new routing strategy is presented based on classifying flows into mice and elephant according to their size to minimize the flow completion time. They use the machine learning technique called association rules to generate the forwarding rules and route each flow according to its class, i.e., mice or elephant.…”

Section: B Related Workmentioning

confidence: 99%

Traffic Classification in IP Networks Through Machine Learning Techniques in Final Systems

Gómez¹,

Riaño²,

Ramírez-González

2023

IEEE Access

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Data centers in higher education institutions, as well as those of large corporations, face challenges in terms of traffic flow management. In some cases, due to the limited hardware resources used for this purpose, and in others, despite having enough high-performance equipment, the centers lag behind when the traffic flow grows exponentially due to the memory limitations of the devices, which slows down the network performance. The contribution of this investigation work is the implementation of a classifying elephant and mice system using machine learning techniques for the early detection with the first flow based on the dynamic calculation of the threshold, according to the input parameters of the final system. In the first instance, training algorithms are used to determine the best performance, then the proposed algorithm determines the model with the best prediction, obtained from the supervised learning algorithm trained in off line mode. Finally in the phase of online prediction, the algorithm is capable of predicting with high precision the type of traffic in terms of the input flow, and updates in a dynamic way the threshold to determine whether the traffic is elephant or mice. With this information the network hardware can decide then to route the flows according to their characterization. According to the results, the model that best generates predictions is the decision tree with a 100% confidence level.

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“…The rest of the packages follow the rule and start flow according to the controller's defined control. Though, there exist some traffic-related issues in SDN on its level which may cause a forwarding loop and traffic congestion, packets drop may also occur due to the said reasons or by facing an unexpected link failure in the network [19].…”

Section: Traffic/packets Loss Issues In Sdn Networkmentioning

confidence: 99%

Intelligent Transmission Control for Efficient Operations in SDN

Alkanhel¹,

Ali²,

Jamil³

et al. 2022

Computers, Materials &Amp; Continua

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Although the Software-Defined Network (SDN) is a well-controlled and efficient network but the complexity of open flow switches in SDN causes multiple issues. Many solutions have been proposed so far for the prevention of errors and mistakes in it but yet, there is still no smooth transmission of pockets from source to destination specifically when irregular movements follow the destination host in SDN, the errors include packet loss, data compromise etc. The accuracy of packets received at their desired destination is possible if networks for pockets and hosts are monitored instead of analysis of network snapshot statistically for the state, as these approaches with open flow switches, discover bugs after their occurrence. This article proposes a design to achieve the said objective by defining the Intelligent Transmission Control Layer (ITCL) layer. It monitors all the connections of end hosts at their specific locations and performs necessary settlements when the connection state changes for one or multiple hosts. The layer informs the controller regarding any state change at one period and controller collects information of end nodes reported via ITCL. Then, updates flow tables accordingly to accommodate a location-change scenario with a routechange policy. ICTL is organized on prototype-based implementation using the popular POX platform. In this paper, it has been discovered that ITCL produces efficient performance in the trafficking of packets and controlling different states of SDN for errors and packet loss.

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On Using Flow Classification to Optimize Traffic Routing in SDN Networks

Cited by 14 publications

References 11 publications

A Comprehensive Survey on Knowledge-Defined Networking

A Comprehensive Survey on Knowledge-Defined Networking

Traffic Classification in IP Networks Through Machine Learning Techniques in Final Systems

Intelligent Transmission Control for Efficient Operations in SDN

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