SDPredictNet-A Topology based SDN Neural Routing Framework with Traffic Prediction Analysis

Sanagavarapu, Sowmya; Sridhar, S.

doi:10.1109/ccwc51732.2021.9376123

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…The spectral clustering technique was used to overcome the arbitrary node distribution of VANETs and to provide flexible grouping based on the graph laplacian's eigen values. The researchers in [13] presented SDPredictNet, a Recurrent Neural Network (RNN) infrastructure that is used on the SDN Controller to forecast network traffic and update the flow table of the superior layer switch for routing based on ostensible network constraints. SDPredictNet makes use of the Sequence-to-Sequence technique to forecast traffic from the SDN that is modelled using ANNs for predicting the direction of packets.…”

Section: Literature Reviewmentioning

confidence: 99%

Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

Balaji¹,

Srinivasan²

2023

Intelligent Automation &Amp; Soft Computing

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Section: Literature Reviewmentioning

confidence: 99%

Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

Balaji¹,

Srinivasan²

2023

Intelligent Automation &Amp; Soft Computing

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“…It also explores the influence of the deep model and the shallow model on the training effect. Deep learning is also used in scenarios such as intelligent channel allocation and traffic prediction [18,20]. Due to the characteristics of data transmission policy for software-defined sensor network, offline algorithms such as deep learning cannot match the dynamic characteristics of network data forwarding.…”

Section: Related Workmentioning

confidence: 99%

Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks

et al. 2022

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Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion.

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“…SDPredictNet, an RNN framework developed by Sowmya Sanagavarapu and implemented on the SDN Controller, can forecast network traffic and alter flow tables of higher layer switches to execute routing depending on anticipated network constraints. SDPredictNet received an RMSE score of 0.07 and a 99.88 percent precision for traffic forecasting and route selection [18].…”

Section: Review Of Literaturementioning

confidence: 99%

Machine Learning Based Network Traffic Predictive Analysis

Srideviponmalar

Kamboj

Shete

2022

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Digital enterprises that use various Internet of Things implementation prototypes, such as cloud, mobile, and edge equipment, are experiencing unprecedented traffic volume and dynamicity. Data center networks (DCN) have faced various issues due to the transient and random nature of traffic created by services and apps. The primary objective of this paper is to predict the network traffic using the machine learning (ML) models before the performance of the network start degrading. Because in the last decade, ML has had a tremendous impact on handling the massive amount of data. With the increase in complexity and traffic, we tried to implement four ML models such as K – Nearest Neighbor (KNN), Random Forest (RF), Gradient Boosting (GB), and Decision Tree (Tree), with tuned sub-parameters to predict the network traffic. We create a matching ML environment based on a sequential database and provide a comparison table of mean square error (MSE), root mean square error (RMSE), mean absolute error (MSE), and coefficient of determination (R2) for each prototype. The simulation results show that the GB with different types is the best-suited model for predicting the network traffic with performance matrix parameters such as MSE 0.001 and RMSE of 0.030. Therefore, the Orange tool is used to stimulate the predictive models.

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SDPredictNet-A Topology based SDN Neural Routing Framework with Traffic Prediction Analysis

Cited by 10 publications

References 30 publications

Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks

Machine Learning Based Network Traffic Predictive Analysis

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