Link capacity estimation in SDN-based end-hosts

Al-Najjar, Anees; Pakzad, Farzaneh; Layeghy, Siamak; Portmann, Marius

doi:10.1109/icspcs.2016.7843372

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…The goal of the NOTED project is to study the relationship between FTS transfers and network traffic in order to trigger actions of an SDN controller to alleviate saturation [2,13]. Therefore we focus on traffic estimation at the time of link overload, defining two main tasks: forecasting the immediate and the short-term evolution of network traffic.…”

Section: The Data Pre-processing Step and Dnn Architecturesmentioning

confidence: 99%

Convolutional LSTM models to estimate network traffic

et al. 2021

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Network utilisation efficiency can, at least in principle, often be improved by dynamically re-configuring routing policies to better distribute ongoing large data transfers. Unfortunately, the information necessary to decide on an appropriate reconfiguration—details of on-going and upcoming data transfers such as their source and destination and, most importantly, their volume and duration—is usually lacking. Fortunately, the increased use of scheduled transfer services, such as FTS, makes it possible to collect the necessary information. However, the mere detection and characterisation of larger transfers is not sufficient to predict with confidence the likelihood a network link will become overloaded. In this paper we present the use of LSTM-based models (CNN-LSTM and Conv-LSTM) to effiectively estimate future network traffic and so provide a solid basis for formulating a sensible network configuration plan.

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Section: The Data Pre-processing Step and Dnn Architecturesmentioning

confidence: 99%

Convolutional LSTM models to estimate network traffic

et al. 2021

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show abstract

Section: The Data Pre-processing Step and Dnn Architecturesmentioning

confidence: 99%

Convolutional LSTM models to estimate network traffic

Waczynska,

Martelli,

Vallecorsa

et al. 2021

Preprint

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Network utilisation efficiency can, at least in principle, often be improved by dynamically re-configuring routing policies to better distribute ongoing large data transfers. Unfortunately, the information necessary to decide on an appropriate reconfiguration-details of on-going and upcoming data transfers such as their source and destination and, most importantly, their volume and duration-is usually lacking. Fortunately, the increased use of scheduled transfer services, such as FTS, makes it possible to collect the necessary information. However, the mere detection and characterisation of larger transfers is not sufficient to predict with confidence the likelihood a network link will become overloaded. In this paper we present the use of LSTM-based models (CNN-LSTM and Conv-LSTM) to effectively estimate future network traffic and so provide a solid basis for formulating a sensible network configuration plan.

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“…The above equation utilises argmax function that selects the interface i that has the maximum bandwidth BW among the available network interfaces N for allocating a new flow. For computing the link bandwidth, a link monitoring module runs in the background that collects and computes link status parameters such as the bandwidth per time basis [28]. The bandwidth computation is briefly explained in section 8.…”

Section: Stateful Link Selectionmentioning

confidence: 99%

“…It is therefore essential to estimate the capacity in advance to optimally distribute the traffic across the available links. An important approach to estimate the capacity is to use Variable Packet Size (VPS) probing adaptive to the concept of SDN [28].…”

Section: Evaluation For Dynamic Link Capacitymentioning

confidence: 99%

Network traffic control for multi‐homed end‐hosts via SDN

2020

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Software Defined Networking (SDN) is an emerging technology of efficiently controlling and managing computer networks, such as in data centres, Wide Area Networks (WANs), as well as in ubiquitous communication. In this paper, we explore the idea of embedding the SDN components, represented by SDN controller and virtual switch, in end-hosts to improve network performance. In particular, we consider load balancing across multiple network interfaces on end-hosts with different link capacity scenarios. We have explored and implemented different SDN-based load balancing approaches based on OpenFlow software switches, and have demonstrated the feasibility and the potential of this approach. The proposed system has been evaluated with multipath transmission control protocol (MPTCP). Our results demonstrated the potential of applying the SDN concepts on multi-homed devices resulting in an increase in achieved throughput of 55% compared to the legacy single network approach and 10% compared to the MPTCP.

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Link capacity estimation in SDN-based end-hosts

Cited by 11 publications

References 15 publications

Convolutional LSTM models to estimate network traffic

Convolutional LSTM models to estimate network traffic

Convolutional LSTM models to estimate network traffic

Network traffic control for multi‐homed end‐hosts via SDN

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